MultiQC Report

General Statistics

Showing ²²¹/₂₂₁ rows and ²²/₄₁ columns.

Sample Name	% rRNA	dupInt	5'-3' bias	M Aligned	Exonic	Intronic	Intergenic	Overlapping Exon	Proper Pairs	Error rate	Non-primary	Reads mapped	% Mapped	% Proper pairs	% MapQ 0 reads	Total seqs	Mean insert	Reads	Reads mapped	% Reads mapped	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	M Unique Reads	% Pass Dedup	Dups	GC	Avg len	Median len	Failed	Seqs	Trimmed bases	Dups	GC	Avg len	Median len	Failed	Seqs
DW-63-V01__RNA__S_S49	0.0	0.0	nan	0.1M	0.0M	0.1M	0.1M	0.0M	75.6%	1.63%	0.0M	0.2M	100.0%	100.0%	2.0%	0.2M	347.3bp	0.3M	0.3M	100.0%	2.1M	0.4M	20.4%	0.4M	18.8%	0.0M	0.0M	28.7%
DW-63-V01__RNA__S_S49_1																													89.8%	75.0%	151bp	151bp	45%	9.6M	82.1%	77.9%	53.0%	124bp	142bp	45%	2.1M
DW-63-V01__RNA__S_S49_2																													96.0%	88.0%	151bp	151bp	45%	9.6M	7.5%	85.0%	56.0%	123bp	137bp	45%	2.1M
DW-63-V02__RNA__S_S50										0.79%	0.0M	0.0M	100.0%	100.0%	1.8%	0.0M	255.8bp	0.0M	0.0M	100.0%	0.2M	0.0M	4.1%	0.0M	3.7%	0.0M	0.0M	36.4%
DW-63-V02__RNA__S_S50_1																													83.8%	80.0%	151bp	151bp	45%	3.6M	95.5%	38.4%	74.0%	127bp	151bp	45%	0.2M
DW-63-V02__RNA__S_S50_2																													98.8%	96.0%	151bp	151bp	55%	3.6M	5.6%	90.4%	86.0%	123bp	151bp	45%	0.2M
DW-63-V03__RNA__S_S51_1																													86.1%	43.0%	151bp	151bp	55%	0.0M	12.5%	89.3%	39.0%	141bp	151bp	45%	0.0M
DW-63-V03__RNA__S_S51_2																													88.3%	45.0%	151bp	151bp	55%	0.0M	6.3%	87.9%	40.0%	141bp	151bp	45%	0.0M
DW-63-V04__RNA__S_S52	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	43.9%	1.78%	0.0M	0.0M	100.0%	100.0%	6.3%	0.0M	368.7bp	0.0M	0.0M	100.0%	0.1M	0.0M	6.3%	0.0M	5.6%	0.0M	0.0M	35.5%
DW-63-V04__RNA__S_S52_1																													55.7%	77.0%	151bp	151bp	45%	0.3M	89.6%	75.1%	58.0%	86bp	72bp	55%	0.1M
DW-63-V04__RNA__S_S52_2																													96.8%	91.0%	151bp	151bp	45%	0.3M	11.8%	89.0%	64.0%	86bp	72bp	55%	0.1M
DW-63-V05__RNA__S_S53	0.1	0.0	nan	0.2M	0.0M	0.1M	0.2M	0.0M	66.8%	2.27%	0.1M	0.4M	100.0%	100.0%	3.2%	0.4M	615.7bp	0.5M	0.5M	100.0%	2.9M	1.3M	45.6%	1.2M	42.1%	0.1M	0.3M	15.8%
DW-63-V05__RNA__S_S53_1																													84.8%	61.0%	151bp	151bp	45%	4.8M	47.9%	84.0%	47.0%	131bp	147bp	27%	2.9M
DW-63-V05__RNA__S_S53_2																													89.2%	69.0%	151bp	151bp	45%	4.8M	9.9%	83.2%	49.0%	130bp	147bp	36%	2.9M
DW-63-V07__RNA__S_S49	0.4	0.0	nan	0.1M	0.0M	0.0M	0.0M	0.0M	68.2%	1.78%	0.0M	0.1M	100.0%	100.0%	2.7%	0.1M	711.7bp	0.1M	0.1M	100.0%	1.0M	0.5M	53.5%	0.5M	49.9%	0.0M	0.1M	11.4%
DW-63-V07__RNA__S_S49_1																													84.1%	71.0%	151bp	151bp	36%	3.1M	73.7%	81.4%	51.0%	122bp	132bp	27%	1.0M
DW-63-V07__RNA__S_S49_2																													95.0%	85.0%	151bp	151bp	55%	3.1M	6.7%	87.1%	53.0%	121bp	132bp	55%	1.0M
DW-63-V08__RNA__S_S50	0.0	0.0	inf	0.0M	0.0M	0.0M	0.0M	0.0M	34.3%	2.68%	0.0M	0.0M	100.0%	100.0%	6.7%	0.0M	1248.8bp	0.0M	0.0M	100.0%	0.2M	0.0M	19.0%	0.0M	16.1%	0.0M	0.0M	8.4%
DW-63-V08__RNA__S_S50_1																													74.2%	57.0%	151bp	151bp	45%	0.3M	41.0%	86.0%	47.0%	120bp	137bp	45%	0.2M
DW-63-V08__RNA__S_S50_2																													85.9%	63.0%	151bp	151bp	55%	0.3M	16.5%	82.9%	48.0%	119bp	137bp	55%	0.2M
DW-63-V09__RNA__S_S51	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	58.5%	1.36%	0.0M	0.0M	100.0%	100.0%	5.0%	0.0M	403.1bp	0.1M	0.1M	100.0%	1.0M	0.1M	9.0%	0.1M	8.0%	0.0M	0.0M	26.1%
DW-63-V09__RNA__S_S51_1																													78.5%	71.0%	151bp	151bp	36%	2.9M	73.5%	71.7%	51.0%	117bp	127bp	27%	1.0M
DW-63-V09__RNA__S_S51_2																													91.2%	84.0%	151bp	151bp	55%	2.9M	8.2%	76.8%	53.0%	117bp	127bp	45%	1.0M
DW-63-V10__RNA__S_S52	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	84.0%	0.90%	0.0M	0.0M	100.0%	100.0%	1.2%	0.0M	370.5bp	0.1M	0.1M	100.0%	0.4M	0.2M	48.8%	0.2M	47.0%	0.0M	0.0M	12.6%
DW-63-V10__RNA__S_S52_1																													78.4%	75.0%	151bp	151bp	36%	2.0M	81.6%	76.5%	54.0%	127bp	151bp	45%	0.4M
DW-63-V10__RNA__S_S52_2																													95.9%	90.0%	151bp	151bp	55%	2.0M	4.1%	84.9%	57.0%	126bp	147bp	55%	0.4M
DW-63-V13__RNA__S_S55	0.3	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	68.4%	1.28%	0.0M	0.0M	100.0%	100.0%	2.7%	0.0M	299.7bp	0.0M	0.0M	100.0%	0.4M	0.0M	5.7%	0.0M	5.2%	0.0M	0.0M	22.8%
DW-63-V13__RNA__S_S55_1																													91.3%	80.0%	151bp	151bp	45%	6.8M	95.4%	53.7%	71.0%	109bp	151bp	55%	0.4M
DW-63-V13__RNA__S_S55_2																													98.9%	95.0%	151bp	151bp	45%	6.8M	6.2%	92.4%	81.0%	106bp	142bp	55%	0.4M
DW-63-V15__RNA__S_S57	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	18.7%	1.34%	0.1M	0.0M	100.0%	100.0%	18.5%	0.0M	89.0bp	0.1M	0.1M	100.0%	0.6M	0.1M	26.0%	0.1M	20.4%	0.0M	0.0M	10.5%
DW-63-V15__RNA__S_S57_1																													81.9%	71.0%	151bp	151bp	55%	2.5M	84.4%	83.7%	33.0%	106bp	107bp	55%	0.6M
DW-63-V15__RNA__S_S57_2																													95.7%	85.0%	151bp	151bp	45%	2.5M	9.9%	92.2%	39.0%	106bp	107bp	45%	0.6M
DW-63-V17__RNA__S_S59	0.0	0.0	0.00	0.0M	0.0M	0.0M	0.0M	0.0M	50.4%	1.85%	0.0M	0.0M	100.0%	100.0%	4.0%	0.0M	365.0bp	0.0M	0.0M	100.0%	0.1M	0.0M	10.9%	0.0M	9.6%	0.0M	0.0M	16.2%
DW-63-V17__RNA__S_S59_1																													67.7%	78.0%	151bp	151bp	45%	0.9M	86.1%	70.9%	67.0%	128bp	151bp	55%	0.1M
DW-63-V17__RNA__S_S59_2																													96.1%	92.0%	151bp	151bp	45%	0.9M	6.4%	81.4%	71.0%	126bp	151bp	45%	0.1M
DW-63-V18__RNA__S_S60										3.00%	0.0M	0.0M	100.0%	100.0%	16.2%	0.0M	287.5bp	0.0M	0.0M	100.0%	0.3M	0.0M	7.6%	0.0M	4.6%	0.0M	0.0M	51.2%
DW-63-V18__RNA__S_S60_1																													80.5%	78.0%	151bp	151bp	45%	3.2M	93.0%	62.3%	51.0%	124bp	151bp	55%	0.3M
DW-63-V18__RNA__S_S60_2																													98.3%	93.0%	151bp	151bp	45%	3.2M	5.0%	87.9%	59.0%	121bp	147bp	45%	0.3M
DW-63-V19__RNA__S_S55	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	56.9%	1.18%	0.0M	0.0M	100.0%	100.0%	4.0%	0.0M	236.1bp	0.0M	0.0M	100.0%	0.1M	0.0M	9.7%	0.0M	8.9%	0.0M	0.0M	13.5%
DW-63-V19__RNA__S_S55_1																													70.1%	78.0%	151bp	151bp	36%	1.0M	90.5%	66.0%	66.0%	114bp	137bp	45%	0.1M
DW-63-V19__RNA__S_S55_2																													97.6%	96.0%	151bp	151bp	55%	1.0M	3.5%	88.5%	73.0%	114bp	137bp	55%	0.1M
DW-63-V20__RNA__S_S56	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	37.7%	3.16%	0.0M	0.0M	100.0%	100.0%	5.1%	0.0M	347.3bp	0.0M	0.0M	100.0%	0.2M	0.0M	13.3%	0.0M	10.9%	0.0M	0.0M	21.2%
DW-63-V20__RNA__S_S56_1																													71.3%	72.0%	151bp	151bp	45%	0.6M	64.7%	85.1%	65.0%	123bp	147bp	45%	0.2M
DW-63-V20__RNA__S_S56_2																													86.6%	83.0%	151bp	151bp	55%	0.6M	9.5%	77.3%	63.0%	122bp	147bp	55%	0.2M
DW-63-V21__RNA__S_S57	0.8	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	61.6%	1.14%	0.0M	0.0M	100.0%	100.0%	3.3%	0.0M	304.9bp	0.0M	0.0M	100.0%	0.2M	0.0M	8.6%	0.0M	7.7%	0.0M	0.0M	11.9%
DW-63-V21__RNA__S_S57_1																													79.4%	78.0%	151bp	151bp	36%	1.6M	91.6%	69.4%	65.0%	102bp	102bp	45%	0.2M
DW-63-V21__RNA__S_S57_2																													98.1%	95.0%	151bp	151bp	55%	1.6M	4.5%	90.8%	72.0%	102bp	102bp	55%	0.2M
DW-63-V22__RNA__S_S58	0.0	0.0	2.65	0.6M	0.2M	0.5M	0.5M	0.0M	80.7%	1.24%	0.2M	1.2M	100.0%	100.0%	1.2%	1.2M	370.0bp	1.4M	1.4M	100.0%	37.7M	5.6M	14.8%	5.3M	13.9%	0.3M	0.7M	11.2%
DW-63-V22__RNA__S_S58_1																													95.6%	53.0%	151bp	151bp	27%	40.5M	20.3%	96.1%	51.0%	129bp	142bp	18%	37.7M
DW-63-V22__RNA__S_S58_2																													94.8%	55.0%	151bp	151bp	45%	40.5M	13.8%	94.8%	51.0%	129bp	142bp	36%	37.7M
DW-63-V24__RNA__S_S60										0.99%	0.0M	0.0M	100.0%	100.0%	4.5%	0.0M	261.8bp	0.0M	0.0M	100.0%	0.3M	0.0M	4.2%	0.0M	3.1%	0.0M	0.0M	36.2%
DW-63-V24__RNA__S_S60_1																													85.4%	80.0%	151bp	151bp	36%	4.1M	94.8%	40.7%	73.0%	118bp	151bp	36%	0.3M
DW-63-V24__RNA__S_S60_2																													98.7%	98.0%	151bp	151bp	55%	4.1M	2.2%	92.9%	88.0%	118bp	151bp	55%	0.3M
DW-63-V26__RNA__S_S62	0.1	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	68.4%	1.28%	0.0M	0.0M	100.0%	100.0%	2.3%	0.0M	376.5bp	0.0M	0.0M	100.0%	0.6M	0.1M	9.8%	0.1M	8.9%	0.0M	0.0M	14.9%
DW-63-V26__RNA__S_S62_1																													86.5%	79.0%	151bp	151bp	45%	5.1M	90.4%	70.4%	66.0%	117bp	147bp	55%	0.6M
DW-63-V26__RNA__S_S62_2																													97.2%	93.0%	151bp	151bp	45%	5.1M	7.2%	86.1%	70.0%	115bp	137bp	45%	0.6M
DW-63-V27__RNA__S_S63	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	38.4%	1.65%	0.0M	0.0M	100.0%	100.0%	4.3%	0.0M	475.3bp	0.0M	0.0M	100.0%	0.1M	0.0M	6.9%	0.0M	5.9%	0.0M	0.0M	23.7%
DW-63-V27__RNA__S_S63_1																													72.3%	79.0%	151bp	151bp	45%	1.6M	93.3%	49.7%	66.0%	128bp	151bp	45%	0.1M
DW-63-V27__RNA__S_S63_2																													98.2%	95.0%	151bp	151bp	55%	1.6M	5.7%	87.6%	74.0%	125bp	151bp	45%	0.1M
DW-63-V28__RNA__S_S64	0.0	0.0	inf	0.0M	0.0M	0.0M	0.0M	0.0M	20.5%	2.71%	0.0M	0.0M	100.0%	100.0%	12.9%	0.0M	1702.6bp	0.0M	0.0M	100.0%	0.1M	0.0M	12.3%	0.0M	9.6%	0.0M	0.0M	26.6%
DW-63-V28__RNA__S_S64_1																													61.7%	76.0%	151bp	151bp	55%	0.4M	66.8%	84.4%	70.0%	131bp	151bp	55%	0.1M
DW-63-V28__RNA__S_S64_2																													86.8%	86.0%	151bp	151bp	45%	0.4M	9.3%	69.1%	68.0%	128bp	151bp	45%	0.1M
DW-63-V29__RNA__S_S65	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	76.0%	1.18%	0.0M	0.0M	100.0%	100.0%	1.4%	0.0M	360.0bp	0.0M	0.0M	100.0%	0.3M	0.2M	53.9%	0.2M	51.5%	0.0M	0.0M	12.1%
DW-63-V29__RNA__S_S65_1																													74.5%	75.0%	151bp	151bp	45%	1.4M	81.0%	76.9%	54.0%	132bp	151bp	45%	0.3M
DW-63-V29__RNA__S_S65_2																													95.9%	88.0%	151bp	151bp	45%	1.4M	6.7%	85.4%	57.0%	131bp	151bp	45%	0.3M
DW-63-V30__RNA__S_S66										4.43%	0.0M	0.0M	100.0%	100.0%	13.6%	0.0M	1002.2bp	0.0M	0.0M	100.0%	0.1M	0.0M	8.6%	0.0M	4.5%	0.0M	0.0M	62.6%
DW-63-V30__RNA__S_S66_1																													65.5%	77.0%	151bp	151bp	45%	0.8M	88.4%	67.0%	55.0%	130bp	151bp	55%	0.1M
DW-63-V30__RNA__S_S66_2																													96.0%	92.0%	151bp	151bp	45%	0.8M	6.5%	76.8%	60.0%	126bp	151bp	45%	0.1M
DW-63-V31__RNA__S_S61	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	31.6%	2.39%	0.0M	0.0M	100.0%	100.0%	6.9%	0.0M	323.1bp	0.0M	0.0M	100.0%	0.1M	0.0M	10.2%	0.0M	8.1%	0.0M	0.0M	13.2%
DW-63-V31__RNA__S_S61_1																													55.4%	75.0%	151bp	151bp	45%	0.4M	79.4%	75.2%	59.0%	124bp	151bp	45%	0.1M
DW-63-V31__RNA__S_S61_2																													92.1%	89.0%	151bp	151bp	55%	0.4M	5.2%	72.5%	60.0%	123bp	147bp	55%	0.1M
DW-63-V32__RNA__S_S62	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	15.9%	2.42%	0.0M	0.0M	100.0%	100.0%	14.0%	0.0M	103.0bp	0.0M	0.0M	100.0%	0.1M	0.0M	13.4%	0.0M	9.9%	0.0M	0.0M	6.2%
DW-63-V32__RNA__S_S62_1																													71.1%	70.0%	151bp	151bp	45%	0.2M	38.7%	84.4%	68.0%	129bp	151bp	45%	0.1M
DW-63-V32__RNA__S_S62_2																													76.9%	75.0%	151bp	151bp	55%	0.2M	12.3%	70.2%	65.0%	128bp	151bp	55%	0.1M
DW-63-V34__RNA__S_S64	0.0	0.0	inf	0.0M	0.0M	0.0M	0.0M	0.0M	42.7%	2.46%	0.0M	0.0M	100.0%	100.0%	5.4%	0.0M	600.2bp	0.0M	0.0M	100.0%	0.2M	0.0M	16.6%	0.0M	14.0%	0.0M	0.0M	8.4%
DW-63-V34__RNA__S_S64_1																													62.9%	75.0%	151bp	151bp	45%	0.5M	68.7%	81.6%	66.0%	129bp	151bp	45%	0.2M
DW-63-V34__RNA__S_S64_2																													88.1%	86.0%	151bp	151bp	55%	0.5M	6.7%	70.8%	64.0%	127bp	151bp	55%	0.2M
DW-63-V35__RNA__S_S65	0.0	0.0	1.33	0.9M	0.7M	0.3M	0.6M	0.1M	78.4%	1.80%	0.2M	1.8M	100.0%	100.0%	0.2%	1.8M	581.9bp	2.0M	2.0M	100.0%	8.6M	4.4M	51.2%	4.0M	46.1%	0.4M	1.0M	20.5%
DW-63-V35__RNA__S_S65_1																													91.7%	62.0%	151bp	151bp	45%	9.2M	26.8%	93.6%	63.0%	117bp	122bp	27%	8.6M
DW-63-V35__RNA__S_S65_2																													91.6%	66.0%	151bp	151bp	55%	9.2M	21.3%	91.7%	64.0%	118bp	122bp	36%	8.6M
DW-63-V36__RNA__S_S66	0.7	0.0	0.00	0.0M	0.0M	0.0M	0.0M	0.0M	72.7%	2.11%	0.0M	0.0M	100.0%	100.0%	2.4%	0.0M	298.4bp	0.0M	0.0M	100.0%	0.3M	0.1M	38.7%	0.1M	36.0%	0.0M	0.0M	11.8%
DW-63-V36__RNA__S_S66_1																													65.4%	68.0%	151bp	151bp	45%	0.5M	57.7%	83.6%	55.0%	126bp	137bp	45%	0.3M
DW-63-V36__RNA__S_S66_2																													89.5%	77.0%	151bp	151bp	55%	0.5M	9.2%	81.1%	55.0%	125bp	137bp	55%	0.3M
DW-63-V37__RNA__S_S67										1.57%	0.0M	0.0M	100.0%	100.0%	17.6%	0.0M	174.2bp	0.0M	0.0M	100.0%	0.4M	0.0M	5.0%	0.0M	3.3%	0.0M	0.0M	27.5%
DW-63-V37__RNA__S_S67_1																													88.7%	79.0%	151bp	151bp	45%	4.0M	93.3%	72.1%	59.0%	106bp	127bp	55%	0.4M
DW-63-V37__RNA__S_S67_2																													98.5%	94.0%	151bp	151bp	45%	4.0M	6.7%	91.8%	66.0%	104bp	117bp	45%	0.4M
DW-63-V38__RNA__S_S68	0.0	0.0	24.71	0.0M	0.0M	0.0M	0.0M	0.0M	46.5%	2.33%	0.0M	0.1M	100.0%	100.0%	4.2%	0.1M	555.4bp	0.1M	0.1M	100.0%	0.7M	0.2M	21.9%	0.1M	19.2%	0.0M	0.1M	30.0%
DW-63-V38__RNA__S_S68_1																													87.9%	75.0%	151bp	151bp	45%	4.5M	87.5%	71.4%	56.0%	123bp	147bp	45%	0.7M
DW-63-V38__RNA__S_S68_2																													92.4%	91.0%	151bp	151bp	45%	4.5M	7.6%	83.8%	62.0%	122bp	147bp	45%	0.7M
DW-63-V40__RNA__S_S70	0.0	0.0	inf	0.0M	0.0M	0.0M	0.0M	0.0M	22.1%	2.79%	0.0M	0.0M	100.0%	100.0%	12.3%	0.0M	434.6bp	0.0M	0.0M	100.0%	0.1M	0.0M	20.6%	0.0M	17.3%	0.0M	0.0M	11.1%
DW-63-V40__RNA__S_S70_1																													63.2%	72.0%	151bp	151bp	55%	0.3M	63.8%	82.7%	60.0%	124bp	151bp	55%	0.1M
DW-63-V40__RNA__S_S70_2																													88.8%	82.0%	151bp	151bp	45%	0.3M	11.4%	78.2%	62.0%	123bp	151bp	45%	0.1M
DW-63-V41__RNA__S_S71	0.0	0.0	inf	0.0M	0.0M	0.0M	0.0M	0.0M	21.7%	3.27%	0.0M	0.0M	100.0%	100.0%	8.4%	0.0M	672.1bp	0.0M	0.0M	100.0%	0.1M	0.0M	10.6%	0.0M	8.0%	0.0M	0.0M	39.0%
DW-63-V41__RNA__S_S71_1																													68.3%	77.0%	151bp	151bp	45%	0.8M	86.9%	71.9%	54.0%	129bp	151bp	55%	0.1M
DW-63-V41__RNA__S_S71_2																													96.1%	91.0%	151bp	151bp	45%	0.8M	6.3%	79.8%	58.0%	126bp	147bp	45%	0.1M
DW-63-V44__RNA__S_S68	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	38.6%	2.55%	0.1M	0.1M	100.0%	100.0%	8.3%	0.1M	474.1bp	0.2M	0.2M	100.0%	0.8M	0.3M	35.5%	0.3M	31.7%	0.0M	0.1M	26.1%
DW-63-V44__RNA__S_S68_1																													76.8%	66.0%	151bp	151bp	45%	1.6M	55.0%	85.5%	52.0%	131bp	147bp	27%	0.8M
DW-63-V44__RNA__S_S68_2																													88.7%	75.0%	151bp	151bp	55%	1.6M	7.7%	80.4%	52.0%	130bp	147bp	45%	0.8M
DW-63-V45__RNA__S_S69	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	53.7%	1.43%	0.0M	0.0M	100.0%	100.0%	3.5%	0.0M	1008.2bp	0.0M	0.0M	100.0%	0.1M	0.1M	44.6%	0.1M	41.5%	0.0M	0.0M	14.1%
DW-63-V45__RNA__S_S69_1																													62.6%	74.0%	151bp	151bp	36%	0.5M	76.7%	78.7%	55.0%	128bp	147bp	45%	0.1M
DW-63-V45__RNA__S_S69_2																													94.8%	88.0%	151bp	151bp	55%	0.5M	4.7%	84.5%	58.0%	128bp	147bp	55%	0.1M
DW-63-V46__RNA__S_S70	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	13.4%	4.20%	0.1M	0.0M	100.0%	100.0%	18.5%	0.0M	2964.9bp	0.1M	0.1M	100.0%	0.4M	0.0M	10.1%	0.0M	5.3%	0.0M	0.1M	62.2%
DW-63-V46__RNA__S_S70_1																													70.2%	61.0%	151bp	151bp	45%	0.8M	59.3%	82.6%	39.0%	125bp	151bp	45%	0.4M
DW-63-V46__RNA__S_S70_2																													87.2%	71.0%	151bp	151bp	55%	0.8M	9.8%	76.0%	38.0%	123bp	147bp	55%	0.4M
DW-63-V47__RNA__S_S71	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	70.4%	1.80%	0.0M	0.0M	100.0%	100.0%	2.3%	0.0M	312.6bp	0.1M	0.1M	100.0%	0.6M	0.3M	51.7%	0.3M	48.8%	0.0M	0.0M	8.1%
DW-63-V47__RNA__S_S71_1																													76.2%	65.0%	151bp	151bp	36%	1.0M	59.2%	86.6%	50.0%	114bp	117bp	18%	0.6M
DW-63-V47__RNA__S_S71_2																													92.9%	75.0%	151bp	151bp	55%	1.0M	14.0%	88.5%	52.0%	114bp	117bp	36%	0.6M
DW-63-V48__RNA__S_S72	0.0	0.0	inf	0.0M	0.0M	0.0M	0.0M	0.0M	70.2%	1.37%	0.0M	0.0M	100.0%	100.0%	2.5%	0.0M	284.8bp	0.0M	0.0M	100.0%	0.2M	0.1M	26.3%	0.1M	24.2%	0.0M	0.0M	12.0%
DW-63-V48__RNA__S_S72_1																													76.1%	78.0%	151bp	151bp	36%	1.4M	89.5%	73.7%	61.0%	103bp	112bp	45%	0.2M
DW-63-V48__RNA__S_S72_2																													97.1%	94.0%	151bp	151bp	55%	1.4M	5.6%	86.8%	65.0%	103bp	112bp	55%	0.2M
DW-63-V49__RNA__S_S73	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	50.4%	1.20%	0.0M	0.0M	100.0%	100.0%	5.5%	0.0M	101.9bp	0.0M	0.0M	100.0%	0.3M	0.0M	6.2%	0.0M	5.2%	0.0M	0.0M	52.5%
DW-63-V49__RNA__S_S73_1																													89.9%	80.0%	151bp	151bp	45%	6.9M	95.9%	22.6%	75.0%	137bp	151bp	36%	0.3M
DW-63-V49__RNA__S_S73_2																													98.4%	96.0%	151bp	151bp	55%	6.9M	5.1%	89.0%	88.0%	132bp	151bp	45%	0.3M
DW-63-V50__RNA__S_S74	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	43.5%	1.03%	0.0M	0.0M	100.0%	100.0%	7.4%	0.0M	141.4bp	0.0M	0.0M	100.0%	0.3M	0.0M	6.2%	0.0M	5.2%	0.0M	0.0M	19.7%
DW-63-V50__RNA__S_S74_1																													79.3%	79.0%	151bp	151bp	45%	3.4M	92.9%	48.8%	69.0%	132bp	151bp	36%	0.3M
DW-63-V50__RNA__S_S74_2																													95.5%	94.0%	151bp	151bp	45%	3.4M	5.3%	84.8%	79.0%	131bp	151bp	45%	0.3M
DW-63-V51__RNA__S_S75	0.0	0.0	1.40	0.6M	0.2M	0.6M	0.3M	0.0M	86.0%	0.84%	0.1M	1.1M	100.0%	100.0%	0.8%	1.1M	364.3bp	1.3M	1.3M	100.0%	6.5M	5.9M	90.2%	5.6M	86.5%	0.2M	0.3M	9.2%
DW-63-V51__RNA__S_S75_1																													88.0%	54.0%	151bp	151bp	45%	8.0M	27.3%	89.6%	48.0%	134bp	147bp	27%	6.5M
DW-63-V51__RNA__S_S75_2																													89.6%	58.0%	151bp	151bp	45%	8.0M	10.0%	87.6%	48.0%	134bp	147bp	18%	6.5M
DW-63-V52__RNA__S_S76	0.0	0.0	inf	0.0M	0.0M	0.0M	0.0M	0.0M	28.7%	2.08%	0.0M	0.0M	100.0%	100.0%	10.3%	0.0M	716.8bp	0.0M	0.0M	100.0%	0.1M	0.0M	11.3%	0.0M	9.3%	0.0M	0.0M	10.7%
DW-63-V52__RNA__S_S76_1																													59.5%	73.0%	151bp	151bp	55%	0.4M	71.7%	78.7%	57.0%	131bp	151bp	55%	0.1M
DW-63-V52__RNA__S_S76_2																													89.3%	85.0%	151bp	151bp	45%	0.4M	8.1%	71.4%	59.0%	130bp	151bp	45%	0.1M
DW-63-V54__RNA__S_S78	0.1	0.0	nan	0.2M	0.0M	0.2M	0.2M	0.0M	84.7%	0.92%	0.1M	0.5M	100.0%	100.0%	1.1%	0.5M	205.2bp	0.5M	0.5M	100.0%	3.6M	3.3M	92.2%	3.2M	88.9%	0.1M	0.3M	7.3%
DW-63-V54__RNA__S_S78_1																													88.1%	52.0%	151bp	151bp	45%	4.4M	26.8%	87.7%	45.0%	135bp	147bp	27%	3.6M
DW-63-V54__RNA__S_S78_2																													89.6%	56.0%	151bp	151bp	45%	4.4M	9.8%	87.7%	46.0%	135bp	147bp	18%	3.6M
DW-63-V56__RNA__S_S74	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	29.5%	1.44%	0.0M	0.0M	100.0%	100.0%	11.9%	0.0M	105.5bp	0.0M	0.0M	100.0%	0.1M	0.0M	12.2%	0.0M	10.1%	0.0M	0.0M	21.9%
DW-63-V56__RNA__S_S74_1																													54.2%	75.0%	151bp	151bp	45%	0.2M	64.9%	81.4%	69.0%	131bp	151bp	45%	0.1M
DW-63-V56__RNA__S_S74_2																													86.9%	86.0%	151bp	151bp	55%	0.2M	6.3%	70.8%	68.0%	131bp	151bp	55%	0.1M
DW-63-V57__RNA__S_S75	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	79.5%	0.78%	0.0M	0.1M	100.0%	100.0%	1.6%	0.1M	157.4bp	0.1M	0.1M	100.0%	0.4M	0.3M	80.5%	0.3M	76.9%	0.0M	0.0M	8.0%
DW-63-V57__RNA__S_S75_1																													71.0%	64.0%	151bp	151bp	36%	0.9M	57.3%	85.6%	47.0%	128bp	137bp	18%	0.4M
DW-63-V57__RNA__S_S75_2																													93.3%	74.0%	151bp	151bp	55%	0.9M	7.9%	87.8%	48.0%	128bp	137bp	45%	0.4M
DW-63-V59__RNA__S_S77	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	49.0%	1.08%	0.0M	0.0M	100.0%	100.0%	5.9%	0.0M	300.1bp	0.0M	0.0M	100.0%	0.1M	0.0M	17.2%	0.0M	13.8%	0.0M	0.0M	11.2%
DW-63-V59__RNA__S_S77_1																													58.0%	75.0%	151bp	151bp	45%	0.4M	75.1%	79.4%	66.0%	121bp	142bp	45%	0.1M
DW-63-V59__RNA__S_S77_2																													93.0%	89.0%	151bp	151bp	55%	0.4M	6.9%	80.6%	67.0%	121bp	142bp	55%	0.1M
DW-63-V60__RNA__S_S78	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	37.1%	1.01%	0.0M	0.0M	100.0%	100.0%	11.6%	0.0M	235.9bp	0.0M	0.0M	100.0%	0.1M	0.0M	8.0%	0.0M	6.8%	0.0M	0.0M	21.4%
DW-63-V60__RNA__S_S78_1																													62.0%	79.0%	151bp	151bp	36%	0.7M	91.0%	61.5%	62.0%	124bp	151bp	45%	0.1M
DW-63-V60__RNA__S_S78_2																													97.6%	96.0%	151bp	151bp	55%	0.7M	2.3%	85.0%	69.0%	125bp	151bp	55%	0.1M
DW-63-V61__RNA__S_S79										1.36%	0.0M	0.0M	100.0%	100.0%	8.6%	0.0M	312.7bp	0.0M	0.0M	100.0%	0.2M	0.0M	2.9%	0.0M	2.3%	0.0M	0.0M	11.4%
DW-63-V61__RNA__S_S79_1																													79.2%	79.0%	151bp	151bp	45%	2.4M	93.3%	63.2%	69.0%	104bp	151bp	55%	0.2M
DW-63-V61__RNA__S_S79_2																													97.9%	94.0%	151bp	151bp	45%	2.4M	7.0%	93.3%	79.0%	103bp	142bp	45%	0.2M
DW-63-V63__RNA__S_S81										2.31%	0.0M	0.0M	100.0%	100.0%	6.0%	0.0M	394.7bp	0.0M	0.0M	100.0%	0.1M	0.0M	5.1%	0.0M	3.9%	0.0M	0.0M	12.2%
DW-63-V63__RNA__S_S81_1																													63.6%	78.0%	151bp	151bp	45%	0.7M	93.0%	47.4%	63.0%	133bp	151bp	45%	0.1M
DW-63-V63__RNA__S_S81_2																													95.4%	94.0%	151bp	151bp	45%	0.7M	5.5%	81.9%	72.0%	131bp	151bp	45%	0.1M
DW-63-V64__RNA__S_S82	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	44.0%	1.30%	0.0M	0.0M	100.0%	100.0%	8.1%	0.0M	109.6bp	0.0M	0.0M	100.0%	0.0M	0.0M	8.3%	0.0M	7.0%	0.0M	0.0M	37.2%
DW-63-V64__RNA__S_S82_1																													48.6%	79.0%	151bp	151bp	45%	0.2M	79.4%	79.1%	74.0%	134bp	151bp	55%	0.0M
DW-63-V64__RNA__S_S82_2																													91.6%	91.0%	151bp	151bp	45%	0.2M	6.4%	68.7%	74.0%	131bp	151bp	45%	0.0M
DW-63-V65__RNA__S_S83	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	80.6%	0.87%	0.0M	0.0M	100.0%	100.0%	1.8%	0.0M	165.9bp	0.0M	0.0M	100.0%	0.1M	0.1M	63.5%	0.1M	61.4%	0.0M	0.0M	7.8%
DW-63-V65__RNA__S_S83_1																													61.5%	73.0%	151bp	151bp	45%	0.5M	76.3%	81.9%	50.0%	133bp	147bp	55%	0.1M
DW-63-V65__RNA__S_S83_2																													96.0%	85.0%	151bp	151bp	45%	0.5M	6.4%	87.7%	52.0%	133bp	147bp	36%	0.1M
DW-63-V66__RNA__S_S84	0.1	0.0	nan	0.2M	0.0M	0.2M	0.2M	0.0M	84.5%	0.94%	0.0M	0.4M	100.0%	100.0%	1.1%	0.4M	180.9bp	0.5M	0.5M	100.0%	3.4M	3.1M	92.7%	3.0M	89.3%	0.1M	0.2M	6.8%
DW-63-V66__RNA__S_S84_1																													86.1%	53.0%	151bp	151bp	36%	4.0M	23.9%	88.6%	48.0%	135bp	147bp	27%	3.4M
DW-63-V66__RNA__S_S84_2																													89.2%	57.0%	151bp	151bp	36%	4.0M	9.8%	87.7%	49.0%	135bp	147bp	18%	3.4M
DW-63-V67__RNA__S_S79	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	41.8%	0.77%	0.0M	0.0M	100.0%	100.0%	10.0%	0.0M	292.5bp	0.0M	0.0M	100.0%	0.1M	0.0M	15.4%	0.0M	12.7%	0.0M	0.0M	7.4%
DW-63-V67__RNA__S_S79_1																													59.1%	77.0%	151bp	151bp	36%	0.5M	86.8%	73.3%	60.0%	126bp	151bp	45%	0.1M
DW-63-V67__RNA__S_S79_2																													96.5%	94.0%	151bp	151bp	55%	0.5M	3.1%	83.8%	65.0%	126bp	151bp	55%	0.1M
DW-63-V68__RNA__S_S80	0.0	0.0	1.33	0.7M	0.3M	0.7M	0.4M	0.0M	85.8%	0.82%	0.1M	1.5M	100.0%	100.0%	0.7%	1.5M	370.6bp	1.6M	1.6M	100.0%	11.3M	10.1M	89.3%	9.6M	85.2%	0.5M	0.8M	7.5%
DW-63-V68__RNA__S_S80_1																													91.6%	51.0%	151bp	151bp	27%	11.4M	13.1%	92.1%	50.0%	133bp	142bp	18%	11.3M
DW-63-V68__RNA__S_S80_2																													89.8%	52.0%	151bp	151bp	45%	11.4M	12.4%	90.0%	50.0%	132bp	142bp	27%	11.3M
DW-63-V69__RNA__S_S81	0.0	0.0	0.00	0.0M	0.0M	0.0M	0.0M	0.0M	22.2%	3.86%	0.0M	0.0M	100.0%	100.0%	14.3%	0.0M	3071.7bp	0.0M	0.0M	100.0%	0.1M	0.0M	10.4%	0.0M	5.2%	0.0M	0.0M	13.6%
DW-63-V69__RNA__S_S81_1																													58.2%	71.0%	151bp	151bp	45%	0.3M	66.4%	82.3%	56.0%	131bp	151bp	45%	0.1M
DW-63-V69__RNA__S_S81_2																													87.4%	81.0%	151bp	151bp	55%	0.3M	6.9%	70.6%	51.0%	127bp	151bp	55%	0.1M
DW-63-V70__RNA__S_S82	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	81.1%	1.03%	0.0M	0.1M	100.0%	100.0%	0.8%	0.1M	582.1bp	0.1M	0.1M	100.0%	0.5M	0.3M	57.5%	0.3M	54.0%	0.0M	0.0M	8.1%
DW-63-V70__RNA__S_S82_1																													79.7%	73.0%	151bp	151bp	36%	1.8M	75.5%	81.3%	55.0%	129bp	147bp	27%	0.5M
DW-63-V70__RNA__S_S82_2																													95.7%	87.0%	151bp	151bp	55%	1.8M	4.5%	87.3%	57.0%	129bp	147bp	55%	0.5M
DW-63-V72__RNA__S_S84	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	82.0%	1.43%	0.0M	0.0M	100.0%	100.0%	0.8%	0.0M	434.4bp	0.1M	0.1M	100.0%	0.7M	0.2M	33.6%	0.2M	31.6%	0.0M	0.0M	10.8%
DW-63-V72__RNA__S_S84_1																													82.3%	75.0%	151bp	151bp	36%	2.5M	77.5%	83.3%	64.0%	119bp	122bp	36%	0.7M
DW-63-V72__RNA__S_S84_2																													96.5%	89.0%	151bp	151bp	55%	2.5M	6.3%	89.6%	66.0%	119bp	122bp	55%	0.7M
DW-63-V73__RNA__S_S85	0.1	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	65.8%	0.85%	0.0M	0.0M	100.0%	100.0%	3.3%	0.0M	192.5bp	0.1M	0.1M	100.0%	0.6M	0.2M	34.4%	0.2M	31.2%	0.0M	0.0M	9.6%
DW-63-V73__RNA__S_S85_1																													80.2%	75.0%	151bp	151bp	45%	2.6M	81.4%	78.3%	55.0%	131bp	151bp	55%	0.6M
DW-63-V73__RNA__S_S85_2																													95.5%	88.0%	151bp	151bp	45%	2.6M	6.4%	83.5%	56.0%	129bp	147bp	45%	0.6M
DW-63-V75__RNA__S_S87	0.0	0.0	inf	0.0M	0.0M	0.0M	0.0M	0.0M	33.1%	3.03%	0.0M	0.0M	100.0%	100.0%	6.2%	0.0M	2776.0bp	0.0M	0.0M	100.0%	0.1M	0.0M	9.8%	0.0M	8.2%	0.0M	0.0M	32.1%
DW-63-V75__RNA__S_S87_1																													66.1%	78.0%	151bp	151bp	45%	0.7M	91.2%	62.7%	60.0%	128bp	151bp	55%	0.1M
DW-63-V75__RNA__S_S87_2																													96.7%	93.0%	151bp	151bp	45%	0.7M	5.3%	83.9%	67.0%	126bp	151bp	45%	0.1M
DW-63-V76__RNA__S_S88	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	39.8%	1.43%	0.0M	0.0M	100.0%	100.0%	8.3%	0.0M	254.1bp	0.0M	0.0M	100.0%	0.1M	0.0M	12.0%	0.0M	9.4%	0.0M	0.0M	11.5%
DW-63-V76__RNA__S_S88_1																													61.2%	78.0%	151bp	151bp	55%	0.3M	62.4%	86.9%	76.0%	136bp	151bp	55%	0.1M
DW-63-V76__RNA__S_S88_2																													83.4%	85.0%	151bp	151bp	45%	0.3M	8.5%	64.0%	71.0%	132bp	151bp	45%	0.1M
DW-63-V77__RNA__S_S89	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	61.8%	1.93%	0.0M	0.0M	100.0%	100.0%	4.3%	0.0M	151.4bp	0.0M	0.0M	100.0%	0.1M	0.0M	8.7%	0.0M	6.5%	0.0M	0.0M	14.8%
DW-63-V77__RNA__S_S89_1																													55.2%	80.0%	151bp	151bp	55%	0.4M	86.9%	70.6%	74.0%	136bp	151bp	55%	0.1M
DW-63-V77__RNA__S_S89_2																													95.0%	93.0%	151bp	151bp	55%	0.4M	6.2%	73.0%	76.0%	132bp	151bp	45%	0.1M
DW-63-V78__RNA__S_S90	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	57.2%	1.10%	0.0M	0.0M	100.0%	100.0%	5.9%	0.0M	115.3bp	0.0M	0.0M	100.0%	0.1M	0.0M	7.9%	0.0M	6.8%	0.0M	0.0M	14.7%
DW-63-V78__RNA__S_S90_1																													60.3%	80.0%	151bp	151bp	45%	0.6M	92.8%	52.5%	68.0%	132bp	151bp	55%	0.1M
DW-63-V78__RNA__S_S90_2																													97.9%	95.0%	151bp	151bp	55%	0.6M	5.1%	87.4%	77.0%	129bp	151bp	45%	0.1M
DW-63-V80__RNA__S_S86	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	76.8%	0.78%	0.0M	0.0M	100.0%	100.0%	2.1%	0.0M	143.1bp	0.0M	0.0M	100.0%	0.2M	0.1M	50.5%	0.1M	48.0%	0.0M	0.0M	6.5%
DW-63-V80__RNA__S_S86_1																													75.9%	63.0%	151bp	151bp	45%	0.3M	37.4%	88.9%	57.0%	128bp	142bp	45%	0.2M
DW-63-V80__RNA__S_S86_2																													84.5%	67.0%	151bp	151bp	55%	0.3M	12.8%	80.8%	55.0%	127bp	142bp	55%	0.2M
DW-63-V82__RNA__S_S88	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	53.5%	0.99%	0.0M	0.0M	100.0%	100.0%	4.9%	0.0M	345.5bp	0.0M	0.0M	100.0%	0.1M	0.0M	11.9%	0.0M	9.6%	0.0M	0.0M	13.1%
DW-63-V82__RNA__S_S88_1																													63.4%	77.0%	151bp	151bp	45%	0.5M	78.0%	78.6%	68.0%	128bp	151bp	45%	0.1M
DW-63-V82__RNA__S_S88_2																													92.9%	91.0%	151bp	151bp	55%	0.5M	4.8%	76.5%	69.0%	127bp	151bp	55%	0.1M
DW-63-V83__RNA__S_S89	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	66.5%	1.02%	0.0M	0.0M	100.0%	100.0%	2.9%	0.0M	290.6bp	0.0M	0.0M	100.0%	0.1M	0.0M	24.1%	0.0M	22.5%	0.0M	0.0M	6.4%
DW-63-V83__RNA__S_S89_1																													59.6%	71.0%	151bp	151bp	36%	0.4M	70.3%	81.5%	55.0%	123bp	142bp	45%	0.1M
DW-63-V83__RNA__S_S89_2																													93.4%	84.0%	151bp	151bp	55%	0.4M	7.2%	84.7%	56.0%	123bp	142bp	55%	0.1M
DW-63-V84__RNA__S_S90	0.0	0.0	inf	0.0M	0.0M	0.0M	0.0M	0.0M	42.3%	1.12%	0.0M	0.0M	100.0%	100.0%	6.4%	0.0M	291.3bp	0.0M	0.0M	100.0%	0.2M	0.0M	8.7%	0.0M	7.0%	0.0M	0.0M	12.6%
DW-63-V84__RNA__S_S90_1																													76.4%	78.0%	151bp	151bp	36%	1.4M	90.1%	75.5%	62.0%	100bp	102bp	45%	0.2M
DW-63-V84__RNA__S_S90_2																													97.5%	94.0%	151bp	151bp	55%	1.4M	5.7%	89.2%	67.0%	100bp	102bp	55%	0.2M
DW-63-V85__RNA__S_S91										2.74%	0.0M	0.0M	100.0%	100.0%	4.0%	0.0M	248.8bp	0.0M	0.0M	100.0%	0.5M	0.0M	2.5%	0.0M	2.0%	0.0M	0.0M	31.0%
DW-63-V85__RNA__S_S91_1																													92.5%	81.0%	151bp	151bp	45%	9.4M	95.0%	53.1%	77.0%	131bp	151bp	55%	0.5M
DW-63-V85__RNA__S_S91_2																													99.0%	96.0%	151bp	151bp	55%	9.4M	5.9%	94.1%	90.0%	126bp	151bp	45%	0.5M
DW-63-V86__RNA__S_S92	0.0	0.0	11.04	0.0M	0.0M	0.0M	0.0M	0.0M	74.1%	0.93%	0.0M	0.0M	100.0%	100.0%	2.3%	0.0M	192.9bp	0.0M	0.0M	100.0%	0.4M	0.1M	30.4%	0.1M	28.3%	0.0M	0.0M	8.0%
DW-63-V86__RNA__S_S92_1																													77.9%	73.0%	151bp	151bp	45%	1.5M	75.7%	81.0%	53.0%	132bp	151bp	45%	0.4M
DW-63-V86__RNA__S_S92_2																													95.4%	85.0%	151bp	151bp	45%	1.5M	7.5%	86.7%	55.0%	131bp	147bp	36%	0.4M
DW-63-V87__RNA__S_S93										0.89%	0.0M	0.0M	100.0%	100.0%	8.0%	0.0M	236.8bp	0.0M	0.0M	100.0%	0.1M	0.0M	4.4%	0.0M	3.9%	0.0M	0.0M	26.1%
DW-63-V87__RNA__S_S93_1																													80.2%	80.0%	151bp	151bp	45%	1.9M	94.9%	47.3%	73.0%	132bp	151bp	36%	0.1M
DW-63-V87__RNA__S_S93_2																													98.9%	95.0%	151bp	151bp	45%	1.9M	4.6%	92.4%	84.0%	128bp	151bp	45%	0.1M
DW-63-V89__RNA__S_S95										1.86%	0.0M	0.0M	100.0%	100.0%	6.8%	0.0M	284.7bp	0.0M	0.0M	100.0%	0.3M	0.0M	5.7%	0.0M	4.6%	0.0M	0.0M	10.5%
DW-63-V89__RNA__S_S95_1																													80.5%	77.0%	151bp	151bp	45%	1.5M	90.9%	81.2%	57.0%	80bp	62bp	55%	0.3M
DW-63-V89__RNA__S_S95_2																													96.9%	91.0%	151bp	151bp	45%	1.5M	12.8%	90.9%	61.0%	80bp	62bp	45%	0.3M
DW-63-V90__RNA__S_S96										1.09%	0.0M	0.0M	100.0%	100.0%	4.6%	0.0M	281.8bp	0.0M	0.0M	100.0%	2.4M	0.0M	0.4%	0.0M	0.4%	0.0M	0.0M	18.2%
DW-63-V90__RNA__S_S96_1																													86.4%	61.0%	151bp	151bp	45%	3.7M	43.3%	92.8%	50.0%	134bp	147bp	27%	2.4M
DW-63-V90__RNA__S_S96_2																													94.8%	67.0%	151bp	151bp	45%	3.7M	8.5%	93.0%	50.0%	134bp	147bp	36%	2.4M
DW-63-V91__RNA__S_S91	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	42.7%	0.89%	0.0M	0.0M	100.0%	100.0%	9.2%	0.0M	98.4bp	0.0M	0.0M	100.0%	0.1M	0.0M	8.5%	0.0M	7.0%	0.0M	0.0M	18.5%
DW-63-V91__RNA__S_S91_1																													73.7%	79.0%	151bp	151bp	36%	1.1M	93.1%	63.9%	67.0%	114bp	147bp	45%	0.1M
DW-63-V91__RNA__S_S91_2																													98.2%	97.0%	151bp	151bp	55%	1.1M	2.5%	88.3%	75.0%	114bp	147bp	55%	0.1M
DW-63-V92__RNA__S_S92	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	80.7%	0.82%	0.0M	0.0M	100.0%	100.0%	1.3%	0.0M	204.5bp	0.0M	0.0M	100.0%	0.0M	0.0M	17.4%	0.0M	16.7%	0.0M	0.0M	18.0%
DW-63-V92__RNA__S_S92_1																													50.3%	80.0%	151bp	151bp	36%	0.4M	94.3%	47.6%	62.0%	141bp	151bp	36%	0.0M
DW-63-V92__RNA__S_S92_2																													98.0%	97.0%	151bp	151bp	55%	0.4M	0.7%	84.2%	71.0%	141bp	151bp	55%	0.0M
DW-63-V93__RNA__S_S93	0.0	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	50.8%	1.52%	0.0M	0.0M	100.0%	100.0%	6.4%	0.0M	305.6bp	0.0M	0.0M	100.0%	0.1M	0.0M	7.7%	0.0M	6.5%	0.0M	0.0M	13.3%
DW-63-V93__RNA__S_S93_1																													76.2%	79.0%	151bp	151bp	36%	1.3M	93.3%	63.1%	65.0%	103bp	122bp	45%	0.1M
DW-63-V93__RNA__S_S93_2																													98.2%	96.0%	151bp	151bp	55%	1.3M	3.8%	91.0%	75.0%	104bp	122bp	55%	0.1M
DW-63-V95__RNA__S_S95	0.9	0.0	nan	0.0M	0.0M	0.0M	0.0M	0.0M	44.5%	1.36%	0.0M	0.0M	100.0%	100.0%	6.0%	0.0M	324.2bp	0.0M	0.0M	100.0%	0.2M	0.0M	8.6%	0.0M	6.8%	0.0M	0.0M	9.9%
DW-63-V95__RNA__S_S95_1																													78.1%	77.0%	151bp	151bp	36%	1.4M	89.4%	78.4%	59.0%	89bp	72bp	45%	0.2M
DW-63-V95__RNA__S_S95_2																													97.1%	93.0%	151bp	151bp	55%	1.4M	8.7%	90.4%	64.0%	90bp	77bp	55%	0.2M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: general_stats_table_table

Sort	Group	Column	Description	ID	Scale
\|\|	Custom content: Biotype Counts	% rRNA	% reads overlapping rRNA features	`custom_content_biotype_counts-percent_rRNA`
\|\|	Custom content: DupRadar	dupInt	Intercept value from DupRadar	`custom_content_dupradar-dupRadar_intercept`
\|\|	QualiMap: RNASeq	5'-3' bias	5'-3' bias	`qualimap_rnaseq-5_3_bias`
\|\|	QualiMap: RNASeq	M Aligned	Reads Aligned (millions)	`qualimap_rnaseq-reads_aligned`	read_count
\|\|	QualiMap: RNASeq	Exonic	Reads aligned to exonic regions	`qualimap_rnaseq-reads_aligned_exonic`	read_count
\|\|	QualiMap: RNASeq	Intronic	Reads aligned to intronic regions	`qualimap_rnaseq-reads_aligned_intronic`	read_count
\|\|	QualiMap: RNASeq	Intergenic	Reads aligned to intergenic regions	`qualimap_rnaseq-reads_aligned_intergenic`	read_count
\|\|	QualiMap: RNASeq	Overlapping Exon	Reads aligned to overlapping exon regions	`qualimap_rnaseq-reads_aligned_overlapping_exon`	read_count
\|\|	RSeQC: Bam Stat	Proper Pairs	% Reads mapped in proper pairs	`rseqc_bam_stat-proper_pairs_percent`
\|\|	Samtools: stats	Error rate	Error rate: mismatches (NM) / bases mapped (CIGAR)	`samtools_stats-error_rate`
\|\|	Samtools: stats	Non-primary	Non-primary alignments (millions)	`samtools_stats-non_primary_alignments`	read_count
\|\|	Samtools: stats	Reads mapped	Reads mapped in the bam file (millions)	`samtools_stats-reads_mapped`	read_count
\|\|	Samtools: stats	% Mapped	% Mapped reads	`samtools_stats-reads_mapped_percent`
\|\|	Samtools: stats	% Proper pairs	% Properly paired reads	`samtools_stats-reads_properly_paired_percent`
\|\|	Samtools: stats	% MapQ 0 reads	% of reads that are ambiguously placed (MapQ=0)	`samtools_stats-reads_MQ0_percent`
\|\|	Samtools: stats	Total seqs	Total sequences in the bam file (millions)	`samtools_stats-raw_total_sequences`	read_count
\|\|	Samtools: stats	Mean insert	Average insert size	`samtools_stats-insert_size_average`
\|\|	Samtools: flagstat	Reads	Total reads in the bam file (millions)	`samtools_flagstat-flagstat_total`	read_count
\|\|	Samtools: flagstat	Reads mapped	Reads mapped in the bam file (millions)	`samtools_flagstat-mapped_passed`	read_count
\|\|	Samtools: flagstat	% Reads mapped	% Reads mapped in the bam file	`samtools_flagstat-mapped_passed_pct`
\|\|	STAR	Total reads	Number of input reads	`star-total_reads`	read_count
\|\|	STAR	Aligned	Mapped reads	`star-mapped`	read_count
\|\|	STAR	Aligned	% Mapped reads	`star-mapped_percent`
\|\|	STAR	Uniq aligned	Uniquely mapped reads	`star-uniquely_mapped`	read_count
\|\|	STAR	Uniq aligned	% Uniquely mapped reads	`star-uniquely_mapped_percent`
\|\|	STAR	Multimapped	Multiple mapped reads	`star-multimapped`	read_count
\|\|	UMI-tools	M Unique Reads	Reads remaining after deduplication (millions)	`umi_tools-dedup_output_reads`	read_count
\|\|	UMI-tools	% Pass Dedup	% processed reads that passed deduplication	`umi_tools-dedup_percent_passing`
\|\|	FastQC (raw)	Dups	% duplicate reads	`fastqc_raw-percent_duplicates`
\|\|	FastQC (raw)	GC	Average % GC content	`fastqc_raw-percent_gc`
\|\|	FastQC (raw)	Avg len	Average read length	`fastqc_raw-avg_sequence_length`
\|\|	FastQC (raw)	Median len	Median read length	`fastqc_raw-median_sequence_length`
\|\|	FastQC (raw)	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_raw-percent_fails`
\|\|	FastQC (raw)	Seqs	Total sequences (millions)	`fastqc_raw-total_sequences`	read_count
\|\|	Cutadapt	Trimmed bases	% total base pairs trimmed	`cutadapt-percent_trimmed`
\|\|	FastQC (trimmed)	Dups	% duplicate reads	`fastqc_trimmed-percent_duplicates`
\|\|	FastQC (trimmed)	GC	Average % GC content	`fastqc_trimmed-percent_gc`
\|\|	FastQC (trimmed)	Avg len	Average read length	`fastqc_trimmed-avg_sequence_length`
\|\|	FastQC (trimmed)	Median len	Median read length	`fastqc_trimmed-median_sequence_length`
\|\|	FastQC (trimmed)	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_trimmed-percent_fails`
\|\|	FastQC (trimmed)	Seqs	Total sequences (millions)	`fastqc_trimmed-total_sequences`	read_count

Sample status checks

Reports on sample strandedness status, and any failures in trimming or mapping.

Strandedness Checks

Showing ¹²⁴/₁₂₄ rows and ⁷/₇ columns.

Sample	Status	Strand inference method	Provided strandedness	Inferred strandedness	Sense (%)	Antisense (%)	Unstranded (%)
DW-63-V01__RNA__S_S49	pass	RSeQC	auto	unstranded	46.6	47.7	5.7
DW-63-V01__RNA__S_S49	pass	Salmon	auto	unstranded	49.6	50.4	0.0
DW-63-V04__RNA__S_S52	fail	RSeQC	auto	unstranded	48.0	50.6	1.4
DW-63-V04__RNA__S_S52	fail	Salmon	auto	undetermined	31.6	68.4	0.0
DW-63-V05__RNA__S_S53	pass	RSeQC	auto	unstranded	46.3	48.3	5.4
DW-63-V05__RNA__S_S53	pass	Salmon	auto	unstranded	49.6	50.4	0.0
DW-63-V07__RNA__S_S49	pass	RSeQC	auto	unstranded	46.8	47.5	5.7
DW-63-V07__RNA__S_S49	pass	Salmon	auto	unstranded	50.1	49.9	0.0
DW-63-V08__RNA__S_S50	pass	RSeQC	auto	unstranded	50.4	43.9	5.8
DW-63-V08__RNA__S_S50	pass	Salmon	auto	unstranded	50.1	49.9	0.0
DW-63-V09__RNA__S_S51	pass	RSeQC	auto	unstranded	47.2	47.4	5.5
DW-63-V09__RNA__S_S51	pass	Salmon	auto	unstranded	53.7	46.3	0.0
DW-63-V10__RNA__S_S52	pass	RSeQC	auto	unstranded	46.3	47.3	6.3
DW-63-V10__RNA__S_S52	pass	Salmon	auto	unstranded	52.1	47.9	0.0
DW-63-V13__RNA__S_S55	pass	RSeQC	auto	unstranded	46.3	48.1	5.6
DW-63-V13__RNA__S_S55	pass	Salmon	auto	unstranded	50.7	49.3	0.0
DW-63-V15__RNA__S_S57	pass	RSeQC	auto	unstranded	48.0	46.0	6.0
DW-63-V15__RNA__S_S57	pass	Salmon	auto	unstranded	48.4	51.6	0.0
DW-63-V17__RNA__S_S59	pass	RSeQC	auto	unstranded	50.3	44.0	5.7
DW-63-V17__RNA__S_S59	pass	Salmon	auto	unstranded	50.3	49.7	0.0
DW-63-V19__RNA__S_S55	pass	RSeQC	auto	unstranded	44.5	48.2	7.3
DW-63-V19__RNA__S_S55	pass	Salmon	auto	unstranded	46.3	53.7	0.0
DW-63-V20__RNA__S_S56	fail	RSeQC	auto	unstranded	52.8	43.6	3.6
DW-63-V20__RNA__S_S56	fail	Salmon	auto	undetermined	43.7	56.3	0.0
DW-63-V21__RNA__S_S57	pass	RSeQC	auto	unstranded	44.1	47.6	8.4
DW-63-V21__RNA__S_S57	pass	Salmon	auto	unstranded	47.1	52.9	0.0
DW-63-V22__RNA__S_S58	pass	RSeQC	auto	unstranded	46.1	47.5	6.4
DW-63-V22__RNA__S_S58	pass	Salmon	auto	unstranded	49.4	50.6	0.0
DW-63-V26__RNA__S_S62	pass	RSeQC	auto	unstranded	44.7	48.6	6.6
DW-63-V26__RNA__S_S62	pass	Salmon	auto	unstranded	51.5	48.5	0.0
DW-63-V27__RNA__S_S63	pass	RSeQC	auto	unstranded	46.2	45.6	8.2
DW-63-V27__RNA__S_S63	pass	Salmon	auto	unstranded	47.0	53.0	0.0
DW-63-V28__RNA__S_S64	fail	RSeQC	auto	unstranded	47.7	46.0	6.3
DW-63-V28__RNA__S_S64	fail	Salmon	auto	undetermined	62.7	37.3	0.0
DW-63-V29__RNA__S_S65	pass	RSeQC	auto	unstranded	46.8	46.4	6.9
DW-63-V29__RNA__S_S65	pass	Salmon	auto	unstranded	51.5	48.5	0.0
DW-63-V31__RNA__S_S61	fail	RSeQC	auto	unstranded	48.9	48.1	3.0
DW-63-V31__RNA__S_S61	fail	Salmon	auto	undetermined	59.9	40.1	0.0
DW-63-V32__RNA__S_S62	fail	RSeQC	auto	unstranded	49.8	44.6	5.7
DW-63-V32__RNA__S_S62	fail	Salmon	auto	undetermined	58.8	41.2	0.0
DW-63-V34__RNA__S_S64	pass	RSeQC	auto	unstranded	48.5	46.0	5.6
DW-63-V34__RNA__S_S64	pass	Salmon	auto	unstranded	50.8	49.2	0.0
DW-63-V35__RNA__S_S65	pass	RSeQC	auto	unstranded	46.8	47.6	5.7
DW-63-V35__RNA__S_S65	pass	Salmon	auto	unstranded	49.5	50.5	0.0
DW-63-V36__RNA__S_S66	pass	RSeQC	auto	unstranded	46.1	48.7	5.2
DW-63-V36__RNA__S_S66	pass	Salmon	auto	unstranded	48.0	52.0	0.0
DW-63-V38__RNA__S_S68	pass	RSeQC	auto	unstranded	45.1	47.5	7.4
DW-63-V38__RNA__S_S68	pass	Salmon	auto	unstranded	48.7	51.3	0.0
DW-63-V40__RNA__S_S70	fail	RSeQC	auto	unstranded	48.7	47.5	3.8
DW-63-V40__RNA__S_S70	fail	Salmon	auto	undetermined	56.2	43.8	0.0
DW-63-V41__RNA__S_S71	pass	RSeQC	auto	unstranded	47.3	49.3	3.4
DW-63-V41__RNA__S_S71	pass	Salmon	auto	unstranded	47.0	53.0	0.0
DW-63-V44__RNA__S_S68	pass	RSeQC	auto	unstranded	45.3	49.3	5.4
DW-63-V44__RNA__S_S68	pass	Salmon	auto	unstranded	51.4	48.6	0.0
DW-63-V45__RNA__S_S69	fail	RSeQC	auto	unstranded	47.3	47.1	5.5
DW-63-V45__RNA__S_S69	fail	Salmon	auto	undetermined	56.0	44.0	0.0
DW-63-V46__RNA__S_S70	pass	RSeQC	auto	unstranded	49.0	48.9	2.1
DW-63-V46__RNA__S_S70	pass	Salmon	auto	unstranded	48.4	51.6	0.0
DW-63-V47__RNA__S_S71	pass	RSeQC	auto	unstranded	46.0	48.6	5.4
DW-63-V47__RNA__S_S71	pass	Salmon	auto	unstranded	48.1	51.9	0.0
DW-63-V48__RNA__S_S72	pass	RSeQC	auto	unstranded	47.2	46.8	6.1
DW-63-V48__RNA__S_S72	pass	Salmon	auto	unstranded	49.3	50.7	0.0
DW-63-V49__RNA__S_S73	pass	RSeQC	auto	unstranded	47.0	48.2	4.8
DW-63-V49__RNA__S_S73	pass	Salmon	auto	unstranded	51.2	48.8	0.0
DW-63-V50__RNA__S_S74	pass	RSeQC	auto	unstranded	44.4	47.7	7.9
DW-63-V50__RNA__S_S74	pass	Salmon	auto	unstranded	45.3	54.7	0.0
DW-63-V51__RNA__S_S75	pass	RSeQC	auto	unstranded	46.8	46.7	6.5
DW-63-V51__RNA__S_S75	pass	Salmon	auto	unstranded	50.0	50.0	0.0
DW-63-V52__RNA__S_S76	fail	RSeQC	auto	unstranded	46.7	48.3	5.0
DW-63-V52__RNA__S_S76	fail	Salmon	auto	undetermined	44.8	55.2	0.0
DW-63-V54__RNA__S_S78	pass	RSeQC	auto	unstranded	46.7	47.0	6.2
DW-63-V54__RNA__S_S78	pass	Salmon	auto	unstranded	50.0	50.0	0.0
DW-63-V56__RNA__S_S74	fail	RSeQC	auto	unstranded	42.4	47.3	10.4
DW-63-V56__RNA__S_S74	fail	Salmon	auto	undetermined	62.0	38.0	0.0
DW-63-V57__RNA__S_S75	pass	RSeQC	auto	unstranded	46.9	46.8	6.3
DW-63-V57__RNA__S_S75	pass	Salmon	auto	unstranded	48.7	51.3	0.0
DW-63-V59__RNA__S_S77	pass	RSeQC	auto	unstranded	39.8	40.2	19.9
DW-63-V59__RNA__S_S77	pass	Salmon	auto	unstranded	50.2	49.8	0.0
DW-63-V60__RNA__S_S78	pass	RSeQC	auto	unstranded	48.9	43.3	7.8
DW-63-V60__RNA__S_S78	pass	Salmon	auto	unstranded	52.4	47.6	0.0
DW-63-V64__RNA__S_S82	fail	RSeQC	auto	unstranded	47.7	41.7	10.5
DW-63-V64__RNA__S_S82	fail	Salmon	auto	undetermined	33.8	66.2	0.0
DW-63-V65__RNA__S_S83	pass	RSeQC	auto	unstranded	46.2	48.0	5.8
DW-63-V65__RNA__S_S83	pass	Salmon	auto	unstranded	49.6	50.4	0.0
DW-63-V66__RNA__S_S84	pass	RSeQC	auto	unstranded	46.7	46.8	6.5
DW-63-V66__RNA__S_S84	pass	Salmon	auto	unstranded	50.4	49.6	0.0
DW-63-V67__RNA__S_S79	pass	RSeQC	auto	unstranded	41.0	43.5	15.6
DW-63-V67__RNA__S_S79	pass	Salmon	auto	unstranded	52.6	47.4	0.0
DW-63-V68__RNA__S_S80	pass	RSeQC	auto	unstranded	46.4	46.7	6.9
DW-63-V68__RNA__S_S80	pass	Salmon	auto	unstranded	50.0	50.0	0.0
DW-63-V69__RNA__S_S81	pass	RSeQC	auto	unstranded	50.8	47.2	2.0
DW-63-V69__RNA__S_S81	pass	Salmon	auto	unstranded	53.8	46.2	0.0
DW-63-V70__RNA__S_S82	pass	RSeQC	auto	unstranded	44.5	45.8	9.6
DW-63-V70__RNA__S_S82	pass	Salmon	auto	unstranded	49.3	50.7	0.0
DW-63-V72__RNA__S_S84	pass	RSeQC	auto	unstranded	47.1	46.0	6.9
DW-63-V72__RNA__S_S84	pass	Salmon	auto	unstranded	50.0	50.0	0.0
DW-63-V73__RNA__S_S85	pass	RSeQC	auto	unstranded	45.4	45.6	9.0
DW-63-V73__RNA__S_S85	pass	Salmon	auto	unstranded	48.5	51.5	0.0
DW-63-V75__RNA__S_S87	fail	RSeQC	auto	unstranded	48.5	44.0	7.6
DW-63-V75__RNA__S_S87	fail	Salmon	auto	undetermined	43.6	56.4	0.0
DW-63-V76__RNA__S_S88	pass	RSeQC	auto	unstranded	42.5	43.7	13.8
DW-63-V76__RNA__S_S88	pass	Salmon	auto	unstranded	47.2	52.8	0.0
DW-63-V77__RNA__S_S89	fail	RSeQC	auto	unstranded	45.7	45.7	8.5
DW-63-V77__RNA__S_S89	fail	Salmon	auto	undetermined	59.9	40.1	0.0
DW-63-V78__RNA__S_S90	fail	RSeQC	auto	unstranded	48.0	44.9	7.1
DW-63-V78__RNA__S_S90	fail	Salmon	auto	undetermined	41.9	58.1	0.0
DW-63-V80__RNA__S_S86	pass	RSeQC	auto	unstranded	47.9	46.1	5.9
DW-63-V80__RNA__S_S86	pass	Salmon	auto	unstranded	49.5	50.5	0.0
DW-63-V82__RNA__S_S88	pass	RSeQC	auto	unstranded	42.1	46.7	11.2
DW-63-V82__RNA__S_S88	pass	Salmon	auto	unstranded	49.5	50.5	0.0
DW-63-V83__RNA__S_S89	pass	RSeQC	auto	unstranded	47.0	44.7	8.3
DW-63-V83__RNA__S_S89	pass	Salmon	auto	unstranded	46.3	53.7	0.0
DW-63-V84__RNA__S_S90	pass	RSeQC	auto	unstranded	44.1	47.2	8.7
DW-63-V84__RNA__S_S90	pass	Salmon	auto	unstranded	53.8	46.2	0.0
DW-63-V86__RNA__S_S92	pass	RSeQC	auto	unstranded	45.9	44.9	9.3
DW-63-V86__RNA__S_S92	pass	Salmon	auto	unstranded	49.6	50.4	0.0
DW-63-V91__RNA__S_S91	pass	RSeQC	auto	unstranded	45.8	43.3	11.0
DW-63-V91__RNA__S_S91	pass	Salmon	auto	unstranded	55.0	45.0	0.0
DW-63-V92__RNA__S_S92	fail	RSeQC	auto	unstranded	48.0	47.9	4.1
DW-63-V92__RNA__S_S92	fail	Salmon	auto	undetermined	34.7	65.3	0.0
DW-63-V93__RNA__S_S93	fail	RSeQC	auto	unstranded	46.0	49.2	4.8
DW-63-V93__RNA__S_S93	fail	Salmon	auto	undetermined	55.9	44.1	0.0
DW-63-V95__RNA__S_S95	pass	RSeQC	auto	unstranded	45.3	47.6	7.1
DW-63-V95__RNA__S_S95	pass	Salmon	auto	unstranded	54.6	45.4	0.0

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fail_strand_check_table_table

Sort	Column	Description	ID
\|\|	Status	Pass if the strandedness inferred by RSeQC matches that provided by the user or predicted by Salmon. Fail otherwise.	`samples_strandedness_check-Status`
\|\|	Strand inference method	Tool used to detect strandedness	`samples_strandedness_check-Strand_inference_method`
\|\|	Provided strandedness	User-provided strandedness	`samples_strandedness_check-Provided_strandedness`
\|\|	Inferred strandedness	Interred strandedness	`samples_strandedness_check-Inferred_strandedness`
\|\|	Sense (%)	Sense (%)	`samples_strandedness_check-Sense`
\|\|	Antisense (%)	Antisense (%)	`samples_strandedness_check-Antisense`
\|\|	Unstranded (%)	Unstranded (%)	`samples_strandedness_check-Unstranded`

WARNING: Fail Alignment Check

Showing ¹¹/₁₁ rows.

Sample	STAR uniquely mapped reads (%)
DW-63-V02__RNA__S_S50	3.7
DW-63-V18__RNA__S_S60	4.6
DW-63-V24__RNA__S_S60	3.1
DW-63-V30__RNA__S_S66	4.5
DW-63-V37__RNA__S_S67	3.3
DW-63-V61__RNA__S_S79	2.3
DW-63-V63__RNA__S_S81	3.9
DW-63-V85__RNA__S_S91	2.0
DW-63-V87__RNA__S_S93	3.9
DW-63-V89__RNA__S_S95	4.6
DW-63-V90__RNA__S_S96	0.4

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fail_mapped_samples_table_table

Sort	Visible	Group	Column	Description	ID	Scale
\|\|			STAR uniquely mapped reads (%)	STAR uniquely mapped reads (%)	`samples_failed_mapping_threshold-STAR_uniquely_mapped_reads`

WARNING: Fail Trimming Check

List of samples that failed the minimum trimmed reads threshold specified via the '--min_trimmed_reads' parameter, and hence were ignored for the downstream processing steps.

Showing ¹/₁ rows.

Sample	Reads after trimming
DW-63-V03__RNA__S_S51	431.0

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fail_trimmed_samples_table_table

Sort	Visible	Group	Column	Description	ID	Scale
\|\|			Reads after trimming	Reads after trimming	`samples_failed_trimming_threshold-Reads_after_trimming`

FastQC (raw)

This section of the report shows FastQC results before adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

All samples have sequences of a single length (151bp)

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Created with MultiQC

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ²⁰/₂₀ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	74	106299789	26.3679%
ATGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	56	360010	0.0893%
TCGCACATTCCGCTTCTACCGGGGGGGTCGAGCTGAGTGGATGCGAATCT	83	250837	0.0622%
CGCACATTCCGCTTCTACCGGGGGGGTCGAGCTGAGTGGATGCGAATCTG	82	201774	0.0501%
NGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	33	159142	0.0395%
GGGGGGGGGGGGGNGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	27	148647	0.0369%
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGNGNGGGGGGG	27	148058	0.0367%
GCACATTCCGCTTCTACCGGGGGGGTCGAGCTGAGTGGATGCGAATCTGT	76	145051	0.0360%
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA	38	80575	0.0200%
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGNGGGGGGGGGGGGGGGG	30	78538	0.0195%
AAGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	34	72919	0.0181%
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGNGGG	28	66777	0.0166%
TAGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	33	63794	0.0158%
CTGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	31	49740	0.0123%
CACGGACTTACGGCCCACCCACAGATTCGCATCCACTCAGCTCGACCCCC	44	45982	0.0114%
CCCACCCACAGATTCGCATCCACTCAGCTCGACCCCCCCGGTAGAAGCGG	39	43672	0.0108%
TTGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	25	43248	0.0107%
AGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	26	40723	0.0101%
CCGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	24	35798	0.0089%
CAGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	22	19963	0.0050%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_raw_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc_raw-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc_raw-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc_raw-total_percent`

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Created with MultiQC

Cutadapt

Finds and removes adapter sequences, primers, poly-A tails, and other types of unwanted sequences.URL: https://cutadapt.readthedocs.ioDOI: 10.14806/ej.17.1.200

Filtered Reads

This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

Created with MultiQC

Trimmed Sequence Lengths (3')

This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.

Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

See the cutadapt documentation for more information on how these numbers are generated.

Created with MultiQC

FastQC (trimmed)

This section of the report shows FastQC results after adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

Created with MultiQC

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Created with MultiQC

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ²⁰/₂₀ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	74	4369980	2.2904%
ATGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	84	365581	0.1916%
TCGCACATTCCGCTTCTACCGGGGGGGTCGAGCTGAGTGGATGCGAATCT	127	306876	0.1608%
CGCACATTCCGCTTCTACCGGGGGGGTCGAGCTGAGTGGATGCGAATCTG	127	251308	0.1317%
GCACATTCCGCTTCTACCGGGGGGGTCGAGCTGAGTGGATGCGAATCTGT	124	195277	0.1024%
ATATATATATATATATATATAT	56	177906	0.0932%
ATATATATATATATATATATATAT	56	157361	0.0825%
ATATATATATATATATATAT	58	141099	0.0740%
CACGGACTTACGGCCCACCCACAGATTCGCATCCACTCAGCTCGACCCCC	122	116360	0.0610%
CCCACCCACAGATTCGCATCCACTCAGCTCGACCCCCCCGGTAGAAGCGG	117	111563	0.0585%
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA	59	96214	0.0504%
AAGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	65	89596	0.0470%
TAGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	69	81245	0.0426%
CACAGATTCGCATCCACTCAGCTCGACCCCCCCGGTAGAAGCGGAATGTG	114	74764	0.0392%
GGCCCACCCACAGATTCGCATCCACTCAGCTCGACCCCCCCGGTAGAAGC	96	70744	0.0371%
CTGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	64	60311	0.0316%
TTGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	62	58324	0.0306%
CGGCCCACCCACAGATTCGCATCCACTCAGCTCGACCCCCCCGGTAGAAG	93	54944	0.0288%
AGGCCCACCCACAGATTCGCATCCACTCAGCTCGACCCCCCCGGTAGAAG	80	37200	0.0195%
CGGACTTACGGCCCACCCACAGATTCGCATCCACTCAGCTCGACCCCCCC	60	22275	0.0117%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_trimmed_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc_trimmed-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc_trimmed-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc_trimmed-total_percent`

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Created with MultiQC

DupRadar

DupRadar provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication. This plot shows the general linear models - a summary of the gene duplication distributions.URL: bioconductor.org/packages/release/bioc/html/dupRadar.html

Created with MultiQC

QualiMap

Quality control of alignment data and its derivatives like feature counts.URL: http://qualimap.bioinfo.cipf.esDOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503

Genomic origin of reads

Classification of mapped reads as originating in exonic, intronic or intergenic regions. These can be displayed as either the number or percentage of mapped reads.

There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. This allows mapped reads to be grouped by whether they originate in an exonic region (for QualiMap, this may include 5′ and 3′ UTR regions as well as protein-coding exons), an intron, or an intergenic region (see the Qualimap 2 documentation).

The inferred genomic origins of RNA-seq reads are presented here as a bar graph showing either the number or percentage of mapped reads in each read dataset that have been assigned to each type of genomic region. This graph can be used to assess the proportion of useful reads in an RNA-seq experiment. That proportion can be reduced by the presence of intron sequences, especially if depletion of ribosomal RNA was used during sample preparation (Sims et al. 2014). It can also be reduced by off-target transcripts, which are detected in greater numbers at the sequencing depths needed to detect poorly-expressed transcripts (Tarazona et al. 2011).

Created with MultiQC

Gene Coverage Profile

Mean distribution of coverage depth across the length of all mapped transcripts.

There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).

QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).

The Normalised plot is calculated by MultiQC to enable comparison of samples with varying sequencing depth. The cumulative mapped-read depth at each position across the averaged transcript position are divided by the total for that sample across the entire averaged transcript.

Created with MultiQC

RSeQC

Evaluates high throughput RNA-seq data.URL: http://rseqc.sourceforge.netDOI: 10.1093/bioinformatics/bts356

Read Distribution

Read Distribution calculates how mapped reads are distributed over genome features.

Created with MultiQC

Inner Distance

Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.

Created with MultiQC

Read Duplication

read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.

Created with MultiQC

Junction Annotation

Junction annotation compares detected splice junctions to a reference gene model. An RNA read can be spliced 2 or more times, each time is called a splicing event.

Created with MultiQC

Junction Saturation

Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.

Click a line to see the data side by side (as in the original RSeQC plot).

Created with MultiQC

Infer experiment

Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).

Created with MultiQC

Bam Stat

All numbers reported in millions.

Created with MultiQC

Showing ⁶²/₆₂ rows and ¹⁴/₁₄ columns.

Sample Name	Total records	QC failed	Duplicates	Non primary hit	Unmapped	Unique	Read-1	Read-2	+ve strand	-ve strand	Non-splice reads	Splice reads	Proper pairs	Different chrom
DW-63-V01__RNA__S_S49	0.3M	0.0M	0.0M	0.0M	0.0M	0.2M	0.1M	0.1M	0.1M	0.1M	0.2M	0.0M	0.2M	0.0M
DW-63-V04__RNA__S_S52	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V05__RNA__S_S53	0.5M	0.0M	0.0M	0.1M	0.0M	0.3M	0.2M	0.2M	0.2M	0.2M	0.3M	0.0M	0.3M	0.0M
DW-63-V07__RNA__S_S49	0.1M	0.0M	0.0M	0.0M	0.0M	0.1M	0.0M	0.0M	0.0M	0.0M	0.1M	0.0M	0.1M	0.0M
DW-63-V08__RNA__S_S50	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V09__RNA__S_S51	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V10__RNA__S_S52	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V13__RNA__S_S55	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V15__RNA__S_S57	0.1M	0.0M	0.0M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V17__RNA__S_S59	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V19__RNA__S_S55	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V20__RNA__S_S56	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V21__RNA__S_S57	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V22__RNA__S_S58	1.4M	0.0M	0.0M	0.2M	0.0M	1.1M	0.6M	0.6M	0.6M	0.6M	1.1M	0.1M	1.1M	0.0M
DW-63-V26__RNA__S_S62	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V27__RNA__S_S63	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V28__RNA__S_S64	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V29__RNA__S_S65	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V31__RNA__S_S61	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V32__RNA__S_S62	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V34__RNA__S_S64	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V35__RNA__S_S65	2.0M	0.0M	0.0M	0.2M	0.0M	1.6M	0.8M	0.8M	0.8M	0.8M	1.4M	0.2M	1.6M	0.0M
DW-63-V36__RNA__S_S66	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V38__RNA__S_S68	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V40__RNA__S_S70	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V41__RNA__S_S71	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V44__RNA__S_S68	0.2M	0.0M	0.0M	0.1M	0.0M	0.1M	0.0M	0.0M	0.0M	0.0M	0.1M	0.0M	0.1M	0.0M
DW-63-V45__RNA__S_S69	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V46__RNA__S_S70	0.1M	0.0M	0.0M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V47__RNA__S_S71	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V48__RNA__S_S72	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V49__RNA__S_S73	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V50__RNA__S_S74	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V51__RNA__S_S75	1.3M	0.0M	0.0M	0.1M	0.0M	1.1M	0.5M	0.5M	0.5M	0.5M	1.0M	0.0M	1.1M	0.0M
DW-63-V52__RNA__S_S76	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V54__RNA__S_S78	0.5M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.2M	0.2M	0.4M	0.0M	0.4M	0.0M
DW-63-V56__RNA__S_S74	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V57__RNA__S_S75	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V59__RNA__S_S77	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V60__RNA__S_S78	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V64__RNA__S_S82	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V65__RNA__S_S83	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V66__RNA__S_S84	0.5M	0.0M	0.0M	0.0M	0.0M	0.4M	0.2M	0.2M	0.2M	0.2M	0.4M	0.0M	0.4M	0.0M
DW-63-V67__RNA__S_S79	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V68__RNA__S_S80	1.6M	0.0M	0.0M	0.1M	0.0M	1.4M	0.7M	0.7M	0.7M	0.7M	1.3M	0.1M	1.4M	0.0M
DW-63-V69__RNA__S_S81	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V70__RNA__S_S82	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V72__RNA__S_S84	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V73__RNA__S_S85	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V75__RNA__S_S87	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V76__RNA__S_S88	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V77__RNA__S_S89	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V78__RNA__S_S90	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V80__RNA__S_S86	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V82__RNA__S_S88	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V83__RNA__S_S89	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V84__RNA__S_S90	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V86__RNA__S_S92	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V91__RNA__S_S91	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V92__RNA__S_S92	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V93__RNA__S_S93	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V95__RNA__S_S95	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: rseqc_bam_stat_table

Sort	Column	Description	ID	Scale
\|\|	Total records	Total records	`total_records`	read_count
\|\|	QC failed	QC failed	`qc_failed`	read_count
\|\|	Duplicates	Optical/PCR duplicate	`optical_pcr_duplicate`	read_count
\|\|	Non primary hit	Non primary hit	`non_primary_hits`	read_count
\|\|	Unmapped	Unmapped reads	`unmapped_reads`	read_count
\|\|	Unique	mapq >= mapq_cut (unique)	`mapq_gte_mapq_cut_unique`	read_count
\|\|	Read-1	Read-1	`read_1`	read_count
\|\|	Read-2	Read-2	`read_2`	read_count
\|\|	+ve strand	Reads map to '+'	`reads_map_to_sense`	read_count
\|\|	-ve strand	Reads map to '-'	`reads_map_to_antisense`	read_count
\|\|	Non-splice reads	Non-splice reads	`non_splice_reads`	read_count
\|\|	Splice reads	Splice reads	`splice_reads`	read_count
\|\|	Proper pairs	Reads mapped in proper pairs	`reads_mapped_in_proper_pairs`	read_count
\|\|	Different chrom	Proper-paired reads map to different chrom	`proper_paired_reads_map_to_different_chrom`	read_count

Samtools

Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

Percent mapped

Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

Created with MultiQC

Alignment stats

This module parses the output from samtools stats. All numbers in millions.

Created with MultiQC

Showing ⁷³/₇₃ rows and ¹³/₁₃ columns.

Sample Name	Total sequences	Mapped & paired	Properly paired	Duplicated	QC Failed	Reads MQ0	Mapped bases (CIGAR)	Bases Trimmed	Duplicated bases	Diff chromosomes	Other orientation	Inward pairs	Outward pairs
DW-63-V01__RNA__S_S49	0.2M	0.2M	0.2M	0.0M	0.0M	0.0M	24.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.1M	0.0M
DW-63-V02__RNA__S_S50	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V04__RNA__S_S52	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V05__RNA__S_S53	0.4M	0.4M	0.4M	0.0M	0.0M	0.0M	39.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.1M	0.1M
DW-63-V07__RNA__S_S49	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	11.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V08__RNA__S_S50	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V09__RNA__S_S51	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	4.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V10__RNA__S_S52	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	6.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V13__RNA__S_S55	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V15__RNA__S_S57	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	3.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V17__RNA__S_S59	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V18__RNA__S_S60	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V19__RNA__S_S55	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V20__RNA__S_S56	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V21__RNA__S_S57	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V22__RNA__S_S58	1.2M	1.2M	1.2M	0.0M	0.0M	0.0M	137.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.4M	0.2M
DW-63-V24__RNA__S_S60	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V26__RNA__S_S62	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V27__RNA__S_S63	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V28__RNA__S_S64	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V29__RNA__S_S65	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	4.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V30__RNA__S_S66	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V31__RNA__S_S61	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V32__RNA__S_S62	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V34__RNA__S_S64	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V35__RNA__S_S65	1.8M	1.8M	1.8M	0.0M	0.0M	0.0M	186.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.6M	0.3M
DW-63-V36__RNA__S_S66	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	2.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V37__RNA__S_S67	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V38__RNA__S_S68	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	6.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V40__RNA__S_S70	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V41__RNA__S_S71	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V44__RNA__S_S68	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	8.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V45__RNA__S_S69	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V46__RNA__S_S70	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	2.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V47__RNA__S_S71	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	4.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V48__RNA__S_S72	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V49__RNA__S_S73	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V50__RNA__S_S74	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V51__RNA__S_S75	1.1M	1.1M	1.1M	0.0M	0.0M	0.0M	143.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.4M	0.2M
DW-63-V52__RNA__S_S76	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V54__RNA__S_S78	0.5M	0.5M	0.5M	0.0M	0.0M	0.0M	58.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.2M	0.1M
DW-63-V56__RNA__S_S74	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V57__RNA__S_S75	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	6.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V59__RNA__S_S77	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V60__RNA__S_S78	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V61__RNA__S_S79	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V63__RNA__S_S81	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V64__RNA__S_S82	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V65__RNA__S_S83	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V66__RNA__S_S84	0.4M	0.4M	0.4M	0.0M	0.0M	0.0M	51.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.1M	0.1M
DW-63-V67__RNA__S_S79	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V68__RNA__S_S80	1.5M	1.5M	1.5M	0.0M	0.0M	0.0M	184.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.5M	0.2M
DW-63-V69__RNA__S_S81	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V70__RNA__S_S82	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	6.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V72__RNA__S_S84	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	5.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V73__RNA__S_S85	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	5.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V75__RNA__S_S87	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V76__RNA__S_S88	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V77__RNA__S_S89	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V78__RNA__S_S90	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V80__RNA__S_S86	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V82__RNA__S_S88	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V83__RNA__S_S89	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V84__RNA__S_S90	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V85__RNA__S_S91	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V86__RNA__S_S92	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	3.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V87__RNA__S_S93	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V89__RNA__S_S95	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V90__RNA__S_S96	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V91__RNA__S_S91	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V92__RNA__S_S92	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V93__RNA__S_S93	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
DW-63-V95__RNA__S_S95	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-stats-dp_table

Sort	Column	Description	ID	Scale
\|\|	Total sequences	Total sequences	`raw_total_sequences`	read_count
\|\|	Mapped & paired	Paired-end technology bit set + both mates mapped	`reads_mapped_and_paired`	read_count
\|\|	Properly paired	Proper-pair bit set	`reads_properly_paired`	read_count
\|\|	Duplicated	PCR or optical duplicate bit set	`reads_duplicated`	read_count
\|\|	QC Failed	QC Failed	`reads_QC_failed`	read_count
\|\|	Reads MQ0	Reads mapped and MQ=0	`reads_MQ0`	read_count
\|\|	Mapped bases (CIGAR)	Mapped bases (CIGAR)	`bases_mapped__cigar`	base_count
\|\|	Bases Trimmed	Bases Trimmed	`bases_trimmed`	base_count
\|\|	Duplicated bases	Duplicated bases	`bases_duplicated`	base_count
\|\|	Diff chromosomes	Pairs on different chromosomes	`pairs_on_different_chromosomes`	read_count
\|\|	Other orientation	Pairs with other orientation	`pairs_with_other_orientation`	read_count
\|\|	Inward pairs	Inward oriented pairs	`inward_oriented_pairs`	read_count
\|\|	Outward pairs	Outward oriented pairs	`outward_oriented_pairs`	read_count

Flagstat

This module parses the output from samtools flagstat

Created with MultiQC

Showing ⁷³/₇₃ rows and ¹¹/₁₁ columns.

Sample Name	Total Reads	Total Passed QC	Mapped	Secondary Alignments	Duplicates	Paired in Sequencing	Properly Paired	Self and mate mapped	Singletons	Mate mapped to diff chr	Diff chr (mapQ >= 5)
DW-63-V01__RNA__S_S49	0.3M	0.3M	0.3M	0.0M	0.0M	0.2M	0.2M	0.2M	0.0M	0.0M	0.0M
DW-63-V02__RNA__S_S50	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V04__RNA__S_S52	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V05__RNA__S_S53	0.5M	0.5M	0.5M	0.1M	0.0M	0.4M	0.4M	0.4M	0.0M	0.0M	0.0M
DW-63-V07__RNA__S_S49	0.1M	0.1M	0.1M	0.0M	0.0M	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M
DW-63-V08__RNA__S_S50	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V09__RNA__S_S51	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V10__RNA__S_S52	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V13__RNA__S_S55	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V15__RNA__S_S57	0.1M	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V17__RNA__S_S59	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V18__RNA__S_S60	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V19__RNA__S_S55	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V20__RNA__S_S56	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V21__RNA__S_S57	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V22__RNA__S_S58	1.4M	1.4M	1.4M	0.2M	0.0M	1.2M	1.2M	1.2M	0.0M	0.0M	0.0M
DW-63-V24__RNA__S_S60	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V26__RNA__S_S62	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V27__RNA__S_S63	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V28__RNA__S_S64	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V29__RNA__S_S65	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V30__RNA__S_S66	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V31__RNA__S_S61	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V32__RNA__S_S62	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V34__RNA__S_S64	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V35__RNA__S_S65	2.0M	2.0M	2.0M	0.2M	0.0M	1.8M	1.8M	1.8M	0.0M	0.0M	0.0M
DW-63-V36__RNA__S_S66	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V37__RNA__S_S67	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V38__RNA__S_S68	0.1M	0.1M	0.1M	0.0M	0.0M	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M
DW-63-V40__RNA__S_S70	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V41__RNA__S_S71	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V44__RNA__S_S68	0.2M	0.2M	0.2M	0.1M	0.0M	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M
DW-63-V45__RNA__S_S69	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V46__RNA__S_S70	0.1M	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V47__RNA__S_S71	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V48__RNA__S_S72	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V49__RNA__S_S73	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V50__RNA__S_S74	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V51__RNA__S_S75	1.3M	1.3M	1.3M	0.1M	0.0M	1.1M	1.1M	1.1M	0.0M	0.0M	0.0M
DW-63-V52__RNA__S_S76	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V54__RNA__S_S78	0.5M	0.5M	0.5M	0.1M	0.0M	0.5M	0.5M	0.5M	0.0M	0.0M	0.0M
DW-63-V56__RNA__S_S74	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V57__RNA__S_S75	0.1M	0.1M	0.1M	0.0M	0.0M	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M
DW-63-V59__RNA__S_S77	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V60__RNA__S_S78	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V61__RNA__S_S79	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V63__RNA__S_S81	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V64__RNA__S_S82	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V65__RNA__S_S83	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V66__RNA__S_S84	0.5M	0.5M	0.5M	0.0M	0.0M	0.4M	0.4M	0.4M	0.0M	0.0M	0.0M
DW-63-V67__RNA__S_S79	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V68__RNA__S_S80	1.6M	1.6M	1.6M	0.1M	0.0M	1.5M	1.5M	1.5M	0.0M	0.0M	0.0M
DW-63-V69__RNA__S_S81	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V70__RNA__S_S82	0.1M	0.1M	0.1M	0.0M	0.0M	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M
DW-63-V72__RNA__S_S84	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V73__RNA__S_S85	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V75__RNA__S_S87	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V76__RNA__S_S88	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V77__RNA__S_S89	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V78__RNA__S_S90	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V80__RNA__S_S86	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V82__RNA__S_S88	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V83__RNA__S_S89	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V84__RNA__S_S90	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V85__RNA__S_S91	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V86__RNA__S_S92	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V87__RNA__S_S93	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V89__RNA__S_S95	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V90__RNA__S_S96	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V91__RNA__S_S91	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V92__RNA__S_S92	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V93__RNA__S_S93	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
DW-63-V95__RNA__S_S95	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-flagstat-table_table

Sort	Column	Description	ID	Scale
\|\|	Total Reads	Total Reads	`flagstat_total`	read_count
\|\|	Total Passed QC	Total Passed QC	`total_passed`	read_count
\|\|	Mapped	Mapped	`mapped_passed`	read_count
\|\|	Secondary Alignments	Secondary Alignments	`secondary_passed`	read_count
\|\|	Duplicates	Duplicates	`duplicates_passed`	read_count
\|\|	Paired in Sequencing	Paired in Sequencing	`paired_in_sequencing_passed`	read_count
\|\|	Properly Paired	Properly Paired	`properly_paired_passed`	read_count
\|\|	Self and mate mapped	Reads with itself and mate mapped	`with_itself_and_mate_mapped_passed`	read_count
\|\|	Singletons	Singletons	`singletons_passed`	read_count
\|\|	Mate mapped to diff chr	Mate mapped to different chromosome	`with_mate_mapped_to_a_different_chr_passed`	read_count
\|\|	Diff chr (mapQ >= 5)	Mate mapped to different chromosome (mapQ >= 5)	`with_mate_mapped_to_a_different_chr_mapQ_5__passed`	read_count

Flagstat: Percentage of total

This module parses the output from samtools flagstat

Created with MultiQC

Showing ⁷³/₇₃ rows and ¹¹/₁₁ columns.

Sample Name	Total Reads	Total Passed QC	Mapped	Secondary Alignments	Duplicates	Paired in Sequencing	Properly Paired	Self and mate mapped	Singletons	Mate mapped to diff chr	Diff chr (mapQ >= 5)
DW-63-V01__RNA__S_S49	100.0%	100.0%	100.0%	17.1%	0.0%	82.9%	82.9%	82.9%	0.0%	0.0%	0.0%
DW-63-V02__RNA__S_S50	100.0%	100.0%	100.0%	14.4%	0.0%	85.6%	85.6%	85.6%	0.0%	0.0%	0.0%
DW-63-V04__RNA__S_S52	100.0%	100.0%	100.0%	41.2%	0.0%	58.8%	58.8%	58.8%	0.0%	0.0%	0.0%
DW-63-V05__RNA__S_S53	100.0%	100.0%	100.0%	23.5%	0.0%	76.5%	76.5%	76.5%	0.0%	0.0%	0.0%
DW-63-V07__RNA__S_S49	100.0%	100.0%	100.0%	23.1%	0.0%	76.9%	76.9%	76.9%	0.0%	0.0%	0.0%
DW-63-V08__RNA__S_S50	100.0%	100.0%	100.0%	46.5%	0.0%	53.5%	53.5%	53.5%	0.0%	0.0%	0.0%
DW-63-V09__RNA__S_S51	100.0%	100.0%	100.0%	30.8%	0.0%	69.2%	69.2%	69.2%	0.0%	0.0%	0.0%
DW-63-V10__RNA__S_S52	100.0%	100.0%	100.0%	11.3%	0.0%	88.7%	88.7%	88.7%	0.0%	0.0%	0.0%
DW-63-V13__RNA__S_S55	100.0%	100.0%	100.0%	22.1%	0.0%	77.9%	77.9%	77.9%	0.0%	0.0%	0.0%
DW-63-V15__RNA__S_S57	100.0%	100.0%	100.0%	64.1%	0.0%	35.9%	35.9%	35.9%	0.0%	0.0%	0.0%
DW-63-V17__RNA__S_S59	100.0%	100.0%	100.0%	35.6%	0.0%	64.4%	64.4%	64.4%	0.0%	0.0%	0.0%
DW-63-V18__RNA__S_S60	100.0%	100.0%	100.0%	65.2%	0.0%	34.8%	34.8%	34.8%	0.0%	0.0%	0.0%
DW-63-V19__RNA__S_S55	100.0%	100.0%	100.0%	32.1%	0.0%	67.9%	67.9%	67.9%	0.0%	0.0%	0.0%
DW-63-V20__RNA__S_S56	100.0%	100.0%	100.0%	43.4%	0.0%	56.6%	56.6%	56.6%	0.0%	0.0%	0.0%
DW-63-V21__RNA__S_S57	100.0%	100.0%	100.0%	28.3%	0.0%	71.7%	71.7%	71.7%	0.0%	0.0%	0.0%
DW-63-V22__RNA__S_S58	100.0%	100.0%	100.0%	13.0%	0.0%	87.0%	87.0%	87.0%	0.0%	0.0%	0.0%
DW-63-V24__RNA__S_S60	100.0%	100.0%	100.0%	28.8%	0.0%	71.2%	71.2%	71.2%	0.0%	0.0%	0.0%
DW-63-V26__RNA__S_S62	100.0%	100.0%	100.0%	22.0%	0.0%	78.0%	78.0%	78.0%	0.0%	0.0%	0.0%
DW-63-V27__RNA__S_S63	100.0%	100.0%	100.0%	45.9%	0.0%	54.1%	54.1%	54.1%	0.0%	0.0%	0.0%
DW-63-V28__RNA__S_S64	100.0%	100.0%	100.0%	64.9%	0.0%	35.1%	35.1%	35.1%	0.0%	0.0%	0.0%
DW-63-V29__RNA__S_S65	100.0%	100.0%	100.0%	16.8%	0.0%	83.2%	83.2%	83.2%	0.0%	0.0%	0.0%
DW-63-V30__RNA__S_S66	100.0%	100.0%	100.0%	66.9%	0.0%	33.1%	33.1%	33.1%	0.0%	0.0%	0.0%
DW-63-V31__RNA__S_S61	100.0%	100.0%	100.0%	51.4%	0.0%	48.6%	48.6%	48.6%	0.0%	0.0%	0.0%
DW-63-V32__RNA__S_S62	100.0%	100.0%	100.0%	65.6%	0.0%	34.4%	34.4%	34.4%	0.0%	0.0%	0.0%
DW-63-V34__RNA__S_S64	100.0%	100.0%	100.0%	41.9%	0.0%	58.1%	58.1%	58.1%	0.0%	0.0%	0.0%
DW-63-V35__RNA__S_S65	100.0%	100.0%	100.0%	11.6%	0.0%	88.4%	88.4%	88.4%	0.0%	0.0%	0.0%
DW-63-V36__RNA__S_S66	100.0%	100.0%	100.0%	19.4%	0.0%	80.6%	80.6%	80.6%	0.0%	0.0%	0.0%
DW-63-V37__RNA__S_S67	100.0%	100.0%	100.0%	64.5%	0.0%	35.5%	35.5%	35.5%	0.0%	0.0%	0.0%
DW-63-V38__RNA__S_S68	100.0%	100.0%	100.0%	39.1%	0.0%	60.9%	60.9%	60.9%	0.0%	0.0%	0.0%
DW-63-V40__RNA__S_S70	100.0%	100.0%	100.0%	62.5%	0.0%	37.5%	37.5%	37.5%	0.0%	0.0%	0.0%
DW-63-V41__RNA__S_S71	100.0%	100.0%	100.0%	58.4%	0.0%	41.6%	41.6%	41.6%	0.0%	0.0%	0.0%
DW-63-V44__RNA__S_S68	100.0%	100.0%	100.0%	50.6%	0.0%	49.4%	49.4%	49.4%	0.0%	0.0%	0.0%
DW-63-V45__RNA__S_S69	100.0%	100.0%	100.0%	36.0%	0.0%	64.0%	64.0%	64.0%	0.0%	0.0%	0.0%
DW-63-V46__RNA__S_S70	100.0%	100.0%	100.0%	68.2%	0.0%	31.8%	31.8%	31.8%	0.0%	0.0%	0.0%
DW-63-V47__RNA__S_S71	100.0%	100.0%	100.0%	21.5%	0.0%	78.5%	78.5%	78.5%	0.0%	0.0%	0.0%
DW-63-V48__RNA__S_S72	100.0%	100.0%	100.0%	21.4%	0.0%	78.6%	78.6%	78.6%	0.0%	0.0%	0.0%
DW-63-V49__RNA__S_S73	100.0%	100.0%	100.0%	38.9%	0.0%	61.1%	61.1%	61.1%	0.0%	0.0%	0.0%
DW-63-V50__RNA__S_S74	100.0%	100.0%	100.0%	42.5%	0.0%	57.5%	57.5%	57.5%	0.0%	0.0%	0.0%
DW-63-V51__RNA__S_S75	100.0%	100.0%	100.0%	9.3%	0.0%	90.7%	90.7%	90.7%	0.0%	0.0%	0.0%
DW-63-V52__RNA__S_S76	100.0%	100.0%	100.0%	53.5%	0.0%	46.5%	46.5%	46.5%	0.0%	0.0%	0.0%
DW-63-V54__RNA__S_S78	100.0%	100.0%	100.0%	10.8%	0.0%	89.2%	89.2%	89.2%	0.0%	0.0%	0.0%
DW-63-V56__RNA__S_S74	100.0%	100.0%	100.0%	54.7%	0.0%	45.3%	45.3%	45.3%	0.0%	0.0%	0.0%
DW-63-V57__RNA__S_S75	100.0%	100.0%	100.0%	14.8%	0.0%	85.2%	85.2%	85.2%	0.0%	0.0%	0.0%
DW-63-V59__RNA__S_S77	100.0%	100.0%	100.0%	35.0%	0.0%	65.0%	65.0%	65.0%	0.0%	0.0%	0.0%
DW-63-V60__RNA__S_S78	100.0%	100.0%	100.0%	46.6%	0.0%	53.4%	53.4%	53.4%	0.0%	0.0%	0.0%
DW-63-V61__RNA__S_S79	100.0%	100.0%	100.0%	41.9%	0.0%	58.1%	58.1%	58.1%	0.0%	0.0%	0.0%
DW-63-V63__RNA__S_S81	100.0%	100.0%	100.0%	42.8%	0.0%	57.2%	57.2%	57.2%	0.0%	0.0%	0.0%
DW-63-V64__RNA__S_S82	100.0%	100.0%	100.0%	41.9%	0.0%	58.1%	58.1%	58.1%	0.0%	0.0%	0.0%
DW-63-V65__RNA__S_S83	100.0%	100.0%	100.0%	14.0%	0.0%	86.0%	86.0%	86.0%	0.0%	0.0%	0.0%
DW-63-V66__RNA__S_S84	100.0%	100.0%	100.0%	10.8%	0.0%	89.2%	89.2%	89.2%	0.0%	0.0%	0.0%
DW-63-V67__RNA__S_S79	100.0%	100.0%	100.0%	40.8%	0.0%	59.2%	59.2%	59.2%	0.0%	0.0%	0.0%
DW-63-V68__RNA__S_S80	100.0%	100.0%	100.0%	9.2%	0.0%	90.8%	90.8%	90.8%	0.0%	0.0%	0.0%
DW-63-V69__RNA__S_S81	100.0%	100.0%	100.0%	53.3%	0.0%	46.7%	46.7%	46.7%	0.0%	0.0%	0.0%
DW-63-V70__RNA__S_S82	100.0%	100.0%	100.0%	11.9%	0.0%	88.1%	88.1%	88.1%	0.0%	0.0%	0.0%
DW-63-V72__RNA__S_S84	100.0%	100.0%	100.0%	11.3%	0.0%	88.7%	88.7%	88.7%	0.0%	0.0%	0.0%
DW-63-V73__RNA__S_S85	100.0%	100.0%	100.0%	23.6%	0.0%	76.4%	76.4%	76.4%	0.0%	0.0%	0.0%
DW-63-V75__RNA__S_S87	100.0%	100.0%	100.0%	49.3%	0.0%	50.7%	50.7%	50.7%	0.0%	0.0%	0.0%
DW-63-V76__RNA__S_S88	100.0%	100.0%	100.0%	44.4%	0.0%	55.6%	55.6%	55.6%	0.0%	0.0%	0.0%
DW-63-V77__RNA__S_S89	100.0%	100.0%	100.0%	27.1%	0.0%	72.9%	72.9%	72.9%	0.0%	0.0%	0.0%
DW-63-V78__RNA__S_S90	100.0%	100.0%	100.0%	31.3%	0.0%	68.7%	68.7%	68.7%	0.0%	0.0%	0.0%
DW-63-V80__RNA__S_S86	100.0%	100.0%	100.0%	17.1%	0.0%	82.9%	82.9%	82.9%	0.0%	0.0%	0.0%
DW-63-V82__RNA__S_S88	100.0%	100.0%	100.0%	32.0%	0.0%	68.0%	68.0%	68.0%	0.0%	0.0%	0.0%
DW-63-V83__RNA__S_S89	100.0%	100.0%	100.0%	22.7%	0.0%	77.3%	77.3%	77.3%	0.0%	0.0%	0.0%
DW-63-V84__RNA__S_S90	100.0%	100.0%	100.0%	42.2%	0.0%	57.8%	57.8%	57.8%	0.0%	0.0%	0.0%
DW-63-V85__RNA__S_S91	100.0%	100.0%	100.0%	25.5%	0.0%	74.5%	74.5%	74.5%	0.0%	0.0%	0.0%
DW-63-V86__RNA__S_S92	100.0%	100.0%	100.0%	17.3%	0.0%	82.7%	82.7%	82.7%	0.0%	0.0%	0.0%
DW-63-V87__RNA__S_S93	100.0%	100.0%	100.0%	37.2%	0.0%	62.8%	62.8%	62.8%	0.0%	0.0%	0.0%
DW-63-V89__RNA__S_S95	100.0%	100.0%	100.0%	44.2%	0.0%	55.8%	55.8%	55.8%	0.0%	0.0%	0.0%
DW-63-V90__RNA__S_S96	100.0%	100.0%	100.0%	30.6%	0.0%	69.4%	69.4%	69.4%	0.0%	0.0%	0.0%
DW-63-V91__RNA__S_S91	100.0%	100.0%	100.0%	41.7%	0.0%	58.3%	58.3%	58.3%	0.0%	0.0%	0.0%
DW-63-V92__RNA__S_S92	100.0%	100.0%	100.0%	13.9%	0.0%	86.1%	86.1%	86.1%	0.0%	0.0%	0.0%
DW-63-V93__RNA__S_S93	100.0%	100.0%	100.0%	35.7%	0.0%	64.3%	64.3%	64.3%	0.0%	0.0%	0.0%
DW-63-V95__RNA__S_S95	100.0%	100.0%	100.0%	39.7%	0.0%	60.3%	60.3%	60.3%	0.0%	0.0%	0.0%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-flagstat-pct-table_table

Sort	Column	Description	ID
\|\|	Total Reads	Total Reads	`flagstat_total_pct`
\|\|	Total Passed QC	Total Passed QC	`total_passed_pct`
\|\|	Mapped	Mapped	`mapped_passed_pct`
\|\|	Secondary Alignments	Secondary Alignments	`secondary_passed_pct`
\|\|	Duplicates	Duplicates	`duplicates_passed_pct`
\|\|	Paired in Sequencing	Paired in Sequencing	`paired_in_sequencing_passed_pct`
\|\|	Properly Paired	Properly Paired	`properly_paired_passed_pct`
\|\|	Self and mate mapped	Self and mate mapped	`with_itself_and_mate_mapped_passed_pct`
\|\|	Singletons	Singletons	`singletons_passed_pct`
\|\|	Mate mapped to diff chr	Mate mapped to diff chr	`with_mate_mapped_to_a_different_chr_passed_pct`
\|\|	Diff chr (mapQ >= 5)	Diff chr (mapQ >= 5)	`with_mate_mapped_to_a_different_chr_mapQ_5__passed_pct`

XY counts

Created with MultiQC

Mapped reads per contig

The samtools idxstats tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

Created with MultiQC

STAR

Universal RNA-seq aligner.URL: https://github.com/alexdobin/STARDOI: 10.1093/bioinformatics/bts635

Summary Statistics

Summary statistics from the STAR alignment

Showing ⁷³/₇₃ rows and ¹⁰/₁₉ columns.

Sample Name	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	Avg. read len	Avg. mapped len	Splices	Annotated splices	GT/AG splices	GC/AG splices	AT/AC splices	Non-canonical splices	Mismatch rate	Del rate	Del len	Ins rate	Ins len
DW-63-V01__RNA__S_S49	2.1M	0.4M	20.4%	0.4M	18.8%	0.0M	247.0bp	212.4bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.1%	0.1%	3.3bp	0.1%	3.0bp
DW-63-V02__RNA__S_S50	0.2M	0.0M	4.1%	0.0M	3.7%	0.0M	250.0bp	244.6bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	1.8bp	0.0%	2.9bp
DW-63-V04__RNA__S_S52	0.1M	0.0M	6.3%	0.0M	5.6%	0.0M	172.0bp	217.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7%	0.0%	1.6bp	0.0%	2.3bp
DW-63-V05__RNA__S_S53	2.9M	1.3M	45.6%	1.2M	42.1%	0.1M	261.0bp	239.5bp	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	1.2%	0.1%	3.7bp	0.1%	3.3bp
DW-63-V07__RNA__S_S49	1.0M	0.5M	53.5%	0.5M	49.9%	0.0M	243.0bp	234.9bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.8%	0.1%	2.9bp	0.0%	2.7bp
DW-63-V08__RNA__S_S50	0.2M	0.0M	19.0%	0.0M	16.1%	0.0M	239.0bp	253.6bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.6%	0.0%	2.1bp	0.0%	1.8bp
DW-63-V09__RNA__S_S51	1.0M	0.1M	9.0%	0.1M	8.0%	0.0M	234.0bp	247.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7%	0.0%	2.0bp	0.0%	2.3bp
DW-63-V10__RNA__S_S52	0.4M	0.2M	48.8%	0.2M	47.0%	0.0M	252.0bp	273.2bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	2.3bp	0.0%	2.0bp
DW-63-V13__RNA__S_S55	0.4M	0.0M	5.7%	0.0M	5.2%	0.0M	215.0bp	214.7bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7%	0.1%	3.7bp	0.0%	3.5bp
DW-63-V15__RNA__S_S57	0.6M	0.1M	26.0%	0.1M	20.4%	0.0M	212.0bp	214.6bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	2.0bp	0.0%	1.5bp
DW-63-V17__RNA__S_S59	0.1M	0.0M	10.9%	0.0M	9.6%	0.0M	253.0bp	255.8bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.6%	0.0%	1.2bp	0.0%	1.2bp
DW-63-V18__RNA__S_S60	0.3M	0.0M	7.6%	0.0M	4.6%	0.0M	244.0bp	223.1bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.0%	0.0%	2.1bp	0.0%	1.4bp
DW-63-V19__RNA__S_S55	0.1M	0.0M	9.7%	0.0M	8.9%	0.0M	228.0bp	237.5bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.1%	3.5bp	0.0%	1.2bp
DW-63-V20__RNA__S_S56	0.2M	0.0M	13.3%	0.0M	10.9%	0.0M	244.0bp	241.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7%	0.0%	1.8bp	0.0%	1.7bp
DW-63-V21__RNA__S_S57	0.2M	0.0M	8.6%	0.0M	7.7%	0.0M	204.0bp	226.0bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.1%	2.8bp	0.0%	1.6bp
DW-63-V22__RNA__S_S58	37.7M	5.6M	14.8%	5.3M	13.9%	0.3M	258.0bp	241.0bp	0.6M	0.5M	0.5M	0.0M	0.0M	0.0M	0.6%	0.0%	2.4bp	0.0%	2.2bp
DW-63-V24__RNA__S_S60	0.3M	0.0M	4.2%	0.0M	3.1%	0.0M	235.0bp	224.2bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	2.2bp	0.0%	2.0bp
DW-63-V26__RNA__S_S62	0.6M	0.1M	9.8%	0.1M	8.9%	0.0M	232.0bp	246.9bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	1.6bp	0.0%	2.0bp
DW-63-V27__RNA__S_S63	0.1M	0.0M	6.9%	0.0M	5.9%	0.0M	253.0bp	253.2bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.6%	0.0%	2.4bp	0.0%	1.6bp
DW-63-V28__RNA__S_S64	0.1M	0.0M	12.3%	0.0M	9.6%	0.0M	259.0bp	251.7bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.8%	0.0%	2.0bp	0.0%	1.5bp
DW-63-V29__RNA__S_S65	0.3M	0.2M	53.9%	0.2M	51.5%	0.0M	263.0bp	278.1bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	2.8bp	0.0%	1.9bp
DW-63-V30__RNA__S_S66	0.1M	0.0M	8.6%	0.0M	4.5%	0.0M	256.0bp	207.2bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.7%	0.1%	3.4bp	0.0%	3.5bp
DW-63-V31__RNA__S_S61	0.1M	0.0M	10.2%	0.0M	8.1%	0.0M	246.0bp	233.0bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.6%	0.0%	1.7bp	0.0%	2.9bp
DW-63-V32__RNA__S_S62	0.1M	0.0M	13.4%	0.0M	9.9%	0.0M	256.0bp	245.9bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7%	0.0%	3.5bp	0.0%	3.0bp
DW-63-V34__RNA__S_S64	0.2M	0.0M	16.6%	0.0M	14.0%	0.0M	254.0bp	248.2bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.8%	0.0%	1.6bp	0.0%	3.5bp
DW-63-V35__RNA__S_S65	8.6M	4.4M	51.2%	4.0M	46.1%	0.4M	234.0bp	242.1bp	1.4M	1.4M	1.3M	0.0M	0.0M	0.0M	1.1%	0.0%	2.2bp	0.0%	1.8bp
DW-63-V36__RNA__S_S66	0.3M	0.1M	38.7%	0.1M	36.0%	0.0M	251.0bp	231.4bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.0%	0.1%	3.7bp	0.1%	3.5bp
DW-63-V37__RNA__S_S67	0.4M	0.0M	5.0%	0.0M	3.3%	0.0M	209.0bp	222.7bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.6%	0.0%	3.0bp	0.0%	1.4bp
DW-63-V38__RNA__S_S68	0.7M	0.2M	21.9%	0.1M	19.2%	0.0M	245.0bp	242.7bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.9%	0.0%	2.8bp	0.0%	2.9bp
DW-63-V40__RNA__S_S70	0.1M	0.0M	20.6%	0.0M	17.3%	0.0M	247.0bp	260.5bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7%	0.0%	2.8bp	0.0%	1.7bp
DW-63-V41__RNA__S_S71	0.1M	0.0M	10.6%	0.0M	8.0%	0.0M	254.0bp	247.0bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.9%	0.0%	2.9bp	0.0%	1.8bp
DW-63-V44__RNA__S_S68	0.8M	0.3M	35.5%	0.3M	31.7%	0.0M	261.0bp	241.5bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.2%	0.1%	3.9bp	0.1%	3.8bp
DW-63-V45__RNA__S_S69	0.1M	0.1M	44.6%	0.1M	41.5%	0.0M	255.0bp	262.4bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	2.2bp	0.0%	2.3bp
DW-63-V46__RNA__S_S70	0.4M	0.0M	10.1%	0.0M	5.3%	0.0M	248.0bp	208.7bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.6%	0.0%	2.9bp	0.0%	2.6bp
DW-63-V47__RNA__S_S71	0.6M	0.3M	51.7%	0.3M	48.8%	0.0M	227.0bp	228.1bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7%	0.1%	3.1bp	0.0%	2.6bp
DW-63-V48__RNA__S_S72	0.2M	0.1M	26.3%	0.1M	24.2%	0.0M	206.0bp	223.9bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.6%	0.0%	2.9bp	0.0%	2.3bp
DW-63-V49__RNA__S_S73	0.3M	0.0M	6.2%	0.0M	5.2%	0.0M	268.0bp	207.9bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.8%	0.0%	2.6bp	0.0%	1.6bp
DW-63-V50__RNA__S_S74	0.3M	0.0M	6.2%	0.0M	5.2%	0.0M	263.0bp	245.5bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	2.2bp	0.0%	2.4bp
DW-63-V51__RNA__S_S75	6.5M	5.9M	90.2%	5.6M	86.5%	0.2M	268.0bp	268.7bp	0.6M	0.6M	0.6M	0.0M	0.0M	0.0M	0.4%	0.0%	2.2bp	0.0%	1.8bp
DW-63-V52__RNA__S_S76	0.1M	0.0M	11.3%	0.0M	9.3%	0.0M	260.0bp	253.4bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.6%	0.0%	2.5bp	0.0%	2.7bp
DW-63-V54__RNA__S_S78	3.6M	3.3M	92.2%	3.2M	88.9%	0.1M	269.0bp	268.5bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	2.2bp	0.0%	1.8bp
DW-63-V56__RNA__S_S74	0.1M	0.0M	12.2%	0.0M	10.1%	0.0M	261.0bp	247.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	1.7bp	0.0%	2.7bp
DW-63-V57__RNA__S_S75	0.4M	0.3M	80.5%	0.3M	76.9%	0.0M	256.0bp	255.7bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3%	0.0%	2.3bp	0.0%	1.9bp
DW-63-V59__RNA__S_S77	0.1M	0.0M	17.2%	0.0M	13.8%	0.0M	241.0bp	256.5bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	1.5bp	0.0%	3.0bp
DW-63-V60__RNA__S_S78	0.1M	0.0M	8.0%	0.0M	6.8%	0.0M	248.0bp	235.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	2.4bp	0.0%	1.0bp
DW-63-V61__RNA__S_S79	0.2M	0.0M	2.9%	0.0M	2.3%	0.0M	206.0bp	218.7bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.6%	0.0%	2.5bp	0.0%	1.9bp
DW-63-V63__RNA__S_S81	0.1M	0.0M	5.1%	0.0M	3.9%	0.0M	263.0bp	230.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.8%	0.1%	6.0bp	0.0%	1.0bp
DW-63-V64__RNA__S_S82	0.0M	0.0M	8.3%	0.0M	7.0%	0.0M	265.0bp	244.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	2.5bp	0.0%	2.5bp
DW-63-V65__RNA__S_S83	0.1M	0.1M	63.5%	0.1M	61.4%	0.0M	265.0bp	266.0bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	2.6bp	0.0%	1.3bp
DW-63-V66__RNA__S_S84	3.4M	3.1M	92.7%	3.0M	89.3%	0.1M	270.0bp	269.2bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	2.3bp	0.0%	2.1bp
DW-63-V67__RNA__S_S79	0.1M	0.0M	15.4%	0.0M	12.7%	0.0M	251.0bp	256.1bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3%	0.0%	2.0bp	0.0%	1.2bp
DW-63-V68__RNA__S_S80	11.3M	10.1M	89.3%	9.6M	85.2%	0.5M	264.0bp	266.8bp	1.5M	1.5M	1.5M	0.0M	0.0M	0.0M	0.4%	0.0%	2.3bp	0.0%	1.9bp
DW-63-V69__RNA__S_S81	0.1M	0.0M	10.4%	0.0M	5.2%	0.0M	257.0bp	192.5bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	2.1%	0.0%	5.1bp	0.0%	2.4bp
DW-63-V70__RNA__S_S82	0.5M	0.3M	57.5%	0.3M	54.0%	0.0M	258.0bp	268.1bp	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	0.5%	0.0%	1.8bp	0.0%	1.8bp
DW-63-V72__RNA__S_S84	0.7M	0.2M	33.6%	0.2M	31.6%	0.0M	238.0bp	242.9bp	0.1M	0.1M	0.1M	0.0M	0.0M	0.0M	0.7%	0.0%	2.1bp	0.0%	1.8bp
DW-63-V73__RNA__S_S85	0.6M	0.2M	34.4%	0.2M	31.2%	0.0M	260.0bp	257.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.0%	1.8bp
DW-63-V75__RNA__S_S87	0.1M	0.0M	9.8%	0.0M	8.2%	0.0M	254.0bp	235.8bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.8%	0.0%	1.6bp	0.0%	1.2bp
DW-63-V76__RNA__S_S88	0.1M	0.0M	12.0%	0.0M	9.4%	0.0M	268.0bp	242.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7%	0.0%	1.7bp	0.0%	2.6bp
DW-63-V77__RNA__S_S89	0.1M	0.0M	8.7%	0.0M	6.5%	0.0M	268.0bp	247.8bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7%	0.0%	1.9bp	0.0%	1.0bp
DW-63-V78__RNA__S_S90	0.1M	0.0M	7.9%	0.0M	6.8%	0.0M	261.0bp	244.6bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	1.0bp	0.0%	1.0bp
DW-63-V80__RNA__S_S86	0.2M	0.1M	50.5%	0.1M	48.0%	0.0M	254.0bp	255.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.3%	0.0%	2.2bp	0.0%	1.6bp
DW-63-V82__RNA__S_S88	0.1M	0.0M	11.9%	0.0M	9.6%	0.0M	254.0bp	250.2bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	2.7bp	0.0%	1.6bp
DW-63-V83__RNA__S_S89	0.1M	0.0M	24.1%	0.0M	22.5%	0.0M	246.0bp	256.2bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	4.6bp	0.0%	1.8bp
DW-63-V84__RNA__S_S90	0.2M	0.0M	8.7%	0.0M	7.0%	0.0M	199.0bp	211.4bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	1.5bp	0.0%	1.5bp
DW-63-V85__RNA__S_S91	0.5M	0.0M	2.5%	0.0M	2.0%	0.0M	256.0bp	214.2bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	1.2%	0.0%	2.0bp	0.0%	6.4bp
DW-63-V86__RNA__S_S92	0.4M	0.1M	30.4%	0.1M	28.3%	0.0M	263.0bp	263.1bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	2.1bp	0.0%	1.7bp
DW-63-V87__RNA__S_S93	0.1M	0.0M	4.4%	0.0M	3.9%	0.0M	260.0bp	229.3bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	2.3bp	0.0%	1.6bp
DW-63-V89__RNA__S_S95	0.3M	0.0M	5.7%	0.0M	4.6%	0.0M	159.0bp	223.2bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.7%	0.0%	1.2bp	0.0%	1.3bp
DW-63-V90__RNA__S_S96	2.4M	0.0M	0.4%	0.0M	0.4%	0.0M	268.0bp	246.7bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	1.1bp	0.0%	1.4bp
DW-63-V91__RNA__S_S91	0.1M	0.0M	8.5%	0.0M	7.0%	0.0M	228.0bp	216.5bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	1.1bp	0.0%	2.7bp
DW-63-V92__RNA__S_S92	0.0M	0.0M	17.4%	0.0M	16.7%	0.0M	281.0bp	266.6bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.4%	0.0%	2.8bp	0.0%	1.3bp
DW-63-V93__RNA__S_S93	0.1M	0.0M	7.7%	0.0M	6.5%	0.0M	206.0bp	201.4bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	1.6bp	0.0%	1.1bp
DW-63-V95__RNA__S_S95	0.2M	0.0M	8.6%	0.0M	6.8%	0.0M	178.0bp	206.9bp	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.5%	0.0%	1.9bp	0.0%	1.1bp

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: star_summary_table_table

Sort	Column	Description	ID	Scale
\|\|	Total reads	Number of input reads	`star-total_reads`	read_count
\|\|	Aligned	Mapped reads	`star-mapped`	read_count
\|\|	Aligned	% Mapped reads	`star-mapped_percent`
\|\|	Uniq aligned	Uniquely mapped reads	`star-uniquely_mapped`	read_count
\|\|	Uniq aligned	% Uniquely mapped reads	`star-uniquely_mapped_percent`
\|\|	Multimapped	Multiple mapped reads	`star-multimapped`	read_count
\|\|	Avg. read len	Average input read length	`star-avg_input_read_length`
\|\|	Avg. mapped len	Average mapped length	`star-avg_mapped_read_length`
\|\|	Splices	Number of splices: Total	`star-num_splices`	read_count
\|\|	Annotated splices	Number of splices: Annotated (sjdb)	`star-num_annotated_splices`	read_count
\|\|	GT/AG splices	Number of splices: GT/AG	`star-num_GTAG_splices`	read_count
\|\|	GC/AG splices	Number of splices: GC/AG	`star-num_GCAG_splices`	read_count
\|\|	AT/AC splices	Number of splices: AT/AC	`star-num_ATAC_splices`	read_count
\|\|	Non-canonical splices	Number of splices: Non-canonical	`star-num_noncanonical_splices`	read_count
\|\|	Mismatch rate	Mismatch rate per base	`star-mismatch_rate`
\|\|	Del rate	Deletion rate per base	`star-deletion_rate`
\|\|	Del len	Deletion average length	`star-deletion_length`
\|\|	Ins rate	Insertion rate per base	`star-insertion_rate`
\|\|	Ins len	Insertion average length	`star-insertion_length`

Alignment Scores

Created with MultiQC

UMI-tools

Tools for dealing with Unique Molecular Identifiers (UMIs)/(RMTs) and scRNA-Seq barcodes.URL: https://github.com/CGATOxford/UMI-toolsDOI: 10.1101/gr.209601.116

Deduplicated Reads

Created with MultiQC

UMI Stats

Statistics from running umi_tools dedup or umi_tools extract

Positions Dedup: Total number of positions deduplicated
Total UMIs: Total UMIs found in sample
Unique UMIs: Unique UMIs found in sample
Mean #UMI: Mean number of unique UMIs per position
Max #UMI: Max number of unique UMIs per position

Created with MultiQC

Showing ⁷³/₇₃ rows and ³/₃ columns.

Sample Name	Positions Dedup	Mean #UMI	Max #UMI
DW-63-V01__RNA__S_S49	5622	1.20	36
DW-63-V02__RNA__S_S50	2741	1.20	54
DW-63-V04__RNA__S_S52	35	2.63	6
DW-63-V05__RNA__S_S53	218789	1.22	86
DW-63-V07__RNA__S_S49	61333	1.41	38
DW-63-V08__RNA__S_S50	590	1.79	17
DW-63-V09__RNA__S_S51	28456	1.15	28
DW-63-V10__RNA__S_S52	26186	1.37	70
DW-63-V13__RNA__S_S55	5657	1.20	62
DW-63-V15__RNA__S_S57	693	1.45	7
DW-63-V17__RNA__S_S59	2416	1.36	16
DW-63-V18__RNA__S_S60	6587	3.75	257
DW-63-V19__RNA__S_S55	1738	1.37	16
DW-63-V20__RNA__S_S56	4075	2.43	112
DW-63-V21__RNA__S_S57	1960	1.32	22
DW-63-V22__RNA__S_S58	605677	1.47	753
DW-63-V24__RNA__S_S60	207	1.77	9
DW-63-V26__RNA__S_S62	8870	1.36	154
DW-63-V27__RNA__S_S63	218	1.67	9
DW-63-V28__RNA__S_S64	144	2.07	8
DW-63-V29__RNA__S_S65	17572	1.34	35
DW-63-V30__RNA__S_S66	4089	3.61	110
DW-63-V31__RNA__S_S61	226	1.83	9
DW-63-V32__RNA__S_S62	349	1.74	7
DW-63-V34__RNA__S_S64	1300	1.53	13
DW-63-V35__RNA__S_S65	437225	2.62	4749
DW-63-V36__RNA__S_S66	406	1.42	5
DW-63-V37__RNA__S_S67	3203	3.77	260
DW-63-V38__RNA__S_S68	28864	2.17	264
DW-63-V40__RNA__S_S70	239	1.99	12
DW-63-V41__RNA__S_S71	3353	2.54	53
DW-63-V44__RNA__S_S68	50999	2.20	441
DW-63-V45__RNA__S_S69	6724	1.92	95
DW-63-V46__RNA__S_S70	13815	5.12	491
DW-63-V47__RNA__S_S71	24630	1.39	34
DW-63-V48__RNA__S_S72	7032	1.26	24
DW-63-V49__RNA__S_S73	580	1.13	5
DW-63-V50__RNA__S_S74	754	1.40	11
DW-63-V51__RNA__S_S75	229110	1.44	47
DW-63-V52__RNA__S_S76	251	1.70	9
DW-63-V54__RNA__S_S78	235234	1.32	42
DW-63-V56__RNA__S_S74	1414	1.85	41
DW-63-V57__RNA__S_S75	27927	1.45	41
DW-63-V59__RNA__S_S77	2311	1.52	67
DW-63-V60__RNA__S_S78	1303	1.55	48
DW-63-V61__RNA__S_S79	216	1.74	9
DW-63-V63__RNA__S_S81	79	1.49	3
DW-63-V64__RNA__S_S82	49	2.96	7
DW-63-V65__RNA__S_S83	6285	1.30	21
DW-63-V66__RNA__S_S84	207814	1.40	24
DW-63-V67__RNA__S_S79	436	1.82	6
DW-63-V68__RNA__S_S80	708172	1.64	583
DW-63-V69__RNA__S_S81	192	1.68	7
DW-63-V70__RNA__S_S82	22954	1.50	12
DW-63-V72__RNA__S_S84	23137	1.55	81
DW-63-V73__RNA__S_S85	6667	1.40	19
DW-63-V75__RNA__S_S87	1902	1.79	24
DW-63-V76__RNA__S_S88	1610	1.43	33
DW-63-V77__RNA__S_S89	1012	1.20	25
DW-63-V78__RNA__S_S90	691	1.23	14
DW-63-V80__RNA__S_S86	7814	1.45	13
DW-63-V82__RNA__S_S88	2217	1.37	44
DW-63-V83__RNA__S_S89	1341	1.55	10
DW-63-V84__RNA__S_S90	430	1.83	11
DW-63-V85__RNA__S_S91	4576	1.14	29
DW-63-V86__RNA__S_S92	3199	1.46	19
DW-63-V87__RNA__S_S93	58	2.88	7
DW-63-V89__RNA__S_S95	253	2.04	13
DW-63-V90__RNA__S_S96	1776	1.32	27
DW-63-V91__RNA__S_S91	136	1.46	4
DW-63-V92__RNA__S_S92	49	1.12	2
DW-63-V93__RNA__S_S93	171	1.70	6
DW-63-V95__RNA__S_S95	352	1.91	16

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: umitools_stats_violin_table

Sort	Column	Description	ID
\|\|	Positions Dedup	Total number of positions deduplicated	`positions_deduplicated`
\|\|	Mean #UMI	Mean number of unique UMIs per position	`mean_umi_per_pos`
\|\|	Max #UMI	Max number of unique UMIs per position	`max_umi_per_pos`

Biotype Counts

Biotype Counts shows reads overlapping genomic features of different biotypes, counted by featureCounts.

Created with MultiQC

Software Versions

Software Versions lists versions of software tools extracted from file contents.

Group	Software	Version
BEDTOOLS_GENOMECOV_FW	bedtools	`2.31.1`
CUSTOM_GETCHROMSIZES	getchromsizes	`1.21`
CUSTOM_TX2GENE	python	`3.10.4`
DupRadar	bioconductor-dupradar	`1.32.0`
FASTQC	fastqc	`0.12.1`
FQ_LINT	fq	`0.12.0 (2024-07-08)`
FQ_SUBSAMPLE	fq	`0.12.0 (2024-07-08)`
GTF_FILTER	python	`3.9.5`
MAKE_TRANSCRIPTS_FASTA	rsem	`1.3.1`
	star	`2.7.10a`
MULTIQC_CUSTOM_BIOTYPE	python	`3.9.5`
QUALIMAP_RNASEQ	qualimap	`2.3`
RSEQC_BAMSTAT	rseqc	`5.0.2`
RSEQC_INFEREXPERIMENT	rseqc	`5.0.2`
RSEQC_INNERDISTANCE	rseqc	`5.0.2`
RSEQC_JUNCTIONANNOTATION	rseqc	`5.0.2`
RSEQC_JUNCTIONSATURATION	rseqc	`5.0.2`
RSEQC_READDISTRIBUTION	rseqc	`5.0.2`
RSEQC_READDUPLICATION	rseqc	`5.0.2`
SALMON_INDEX	salmon	`1.10.3`
SALMON_QUANT	salmon	`1.10.3`
SAMTOOLS_FLAGSTAT	samtools	`1.21`
SAMTOOLS_IDXSTATS	samtools	`1.21`
SAMTOOLS_INDEX	samtools	`1.21`
SAMTOOLS_SORT	samtools	`1.21`
SAMTOOLS_STATS	samtools	`1.21`
SE_GENE_UNIFIED	bioconductor-summarizedexperiment	`1.32.0`
SE_TRANSCRIPT_UNIFIED	bioconductor-summarizedexperiment	`1.32.0`
STAR_ALIGN_IGENOMES	gawk	`5.1.0`
	samtools	`1.1`
	star	`2.6.1d`
STRINGTIE_STRINGTIE	stringtie	`2.2.3`
SUBREAD_FEATURECOUNTS	subread	`2.0.6`
TRIMGALORE	cutadapt	`4.9`
	pigz	`2.8`
	trimgalore	`0.6.10`
TXIMETA_TXIMPORT	bioconductor-tximeta	`1.20.1`
UCSC_BEDCLIP	ucsc	`377`
UCSC_BEDGRAPHTOBIGWIG	ucsc	`469`
UMITOOLS_DEDUP	umitools	`1.1.5`
UMITOOLS_PREPAREFORRSEM	umitools	`1.1.5`
Workflow	Nextflow	`25.04.6`
	nf-core/rnaseq	`v3.19.0-g0bb032c`

nf-core/rnaseq Methods Description

Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/rnaseq

Methods

Data was processed using nf-core/rnaseq v3.19.0 (doi: 10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

The pipeline was executed with Nextflow v25.04.6 (Di Tommaso et al., 2017) with the following command:

nextflow run nf-core/rnaseq -profile conda --input samples.csv --outdir /raid/VIDRL-USERS/HOME/aduncan/projects/nf-pipeline/validation_plate/nextflow_output --genome GRCh37 --with_umi --umitools_umi_separator ':' --skip_umi_extract --skip_deseq2_qc

References

Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192

Notes:

The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

nf-core/rnaseq Workflow Summary

- this information is collected when the pipeline is started.URL: https://github.com/nf-core/rnaseq

Input/output options

input: samples.csv
outdir: /raid/VIDRL-USERS/HOME/aduncan/projects/nf-pipeline/validation_plate/nextflow_output

Reference genome options

fasta: s3://ngi-igenomes/igenomes//Homo_sapiens/Ensembl/GRCh37/Sequence/WholeGenomeFasta/genome.fa
gene_bed: s3://ngi-igenomes/igenomes//Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.bed
genome: GRCh37
gtf: s3://ngi-igenomes/igenomes//Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.gtf
star_index: s3://ngi-igenomes/igenomes//Homo_sapiens/Ensembl/GRCh37/Sequence/STARIndex/

UMI options

umitools_umi_separator: :
with_umi: true

Process skipping options

skip_deseq2_qc: true
skip_umi_extract: true

Generic options

trace_report_suffix: 2025-08-01_11-22-59

Core Nextflow options

configFiles: /raid/VIDRL-USERS/HOME/aduncan/.nextflow/assets/nf-core/rnaseq/nextflow.config, /raid/VIDRL-USERS/HOME/aduncan/projects/nf-pipeline/nextflow.config
launchDir: /raid/VIDRL-USERS/HOME/aduncan/projects/nf-pipeline
profile: conda
projectDir: /raid/VIDRL-USERS/HOME/aduncan/.nextflow/assets/nf-core/rnaseq
revision: master
runName: fabulous_lalande
userName: aduncan
workDir: /raid/VIDRL-USERS/HOME/aduncan/projects/nf-pipeline/work

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